{"cells": [{"cell_type": "markdown", "metadata": {"slideshow": {"slide_type": "slide"}}, "source": ["# Part 5 - Working with Time Series Data"]}, {"cell_type": "markdown", "metadata": {"toc": true}, "source": ["<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Date-and-Time-in-Python\" data-toc-modified-id=\"Date-and-Time-in-Python-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Date and Time in Python</a></span><ul class=\"toc-item\"><li><span><a href=\"#Building-datetime-object\" data-toc-modified-id=\"Building-datetime-object-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Building <code>datetime</code> object</a></span></li><li><span><a href=\"#Converting-between-String-and-DateTime\" data-toc-modified-id=\"Converting-between-String-and-DateTime-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Converting between String and DateTime</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-strftime-and-strptime\" data-toc-modified-id=\"Using-strftime-and-strptime-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Using <code>strftime</code> and <code>strptime</code></a></span></li><li><span><a href=\"#Using-parser.parse\" data-toc-modified-id=\"Using-parser.parse-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Using <code>parser.parse</code></a></span></li></ul></li></ul></li><li><span><a href=\"#Date-and-Time-in-Pandas\" data-toc-modified-id=\"Date-and-Time-in-Pandas-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Date and Time in Pandas</a></span><ul class=\"toc-item\"><li><span><a href=\"#The-Basics\" data-toc-modified-id=\"The-Basics-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>The Basics</a></span><ul class=\"toc-item\"><li><span><a href=\"#DatetimeIndex\" data-toc-modified-id=\"DatetimeIndex-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span><code>DatetimeIndex</code></a></span></li><li><span><a href=\"#PeriodIndex\" data-toc-modified-id=\"PeriodIndex-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span><code>PeriodIndex</code></a></span><ul class=\"toc-item\"><li><span><a href=\"#Arithmetic-Operations\" data-toc-modified-id=\"Arithmetic-Operations-2.1.2.1\"><span class=\"toc-item-num\">2.1.2.1&nbsp;&nbsp;</span>Arithmetic Operations</a></span></li></ul></li><li><span><a href=\"#TimedeltaIndex\" data-toc-modified-id=\"TimedeltaIndex-2.1.3\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span><code>TimedeltaIndex</code></a></span></li></ul></li><li><span><a href=\"#Date-Range-and-Frequency\" data-toc-modified-id=\"Date-Range-and-Frequency-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Date Range and Frequency</a></span><ul class=\"toc-item\"><li><span><a href=\"#Combining-Frequency-Codes\" data-toc-modified-id=\"Combining-Frequency-Codes-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Combining Frequency Codes</a></span></li></ul></li><li><span><a href=\"#Indexing-and-Selection\" data-toc-modified-id=\"Indexing-and-Selection-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Indexing and Selection</a></span></li><li><span><a href=\"#Resampling,-Shifting,-and-Windowing\" data-toc-modified-id=\"Resampling,-Shifting,-and-Windowing-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Resampling, Shifting, and Windowing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Resampling\" data-toc-modified-id=\"Resampling-2.4.1\"><span class=\"toc-item-num\">2.4.1&nbsp;&nbsp;</span>Resampling</a></span><ul class=\"toc-item\"><li><span><a href=\"#Downsample-Plot\" data-toc-modified-id=\"Downsample-Plot-2.4.1.1\"><span class=\"toc-item-num\">2.4.1.1&nbsp;&nbsp;</span>Downsample Plot</a></span></li><li><span><a href=\"#Upsample-Plot\" data-toc-modified-id=\"Upsample-Plot-2.4.1.2\"><span class=\"toc-item-num\">2.4.1.2&nbsp;&nbsp;</span>Upsample Plot</a></span></li></ul></li><li><span><a href=\"#Shifting\" data-toc-modified-id=\"Shifting-2.4.2\"><span class=\"toc-item-num\">2.4.2&nbsp;&nbsp;</span>Shifting</a></span><ul class=\"toc-item\"><li><span><a href=\"#Plot-the-Data\" data-toc-modified-id=\"Plot-the-Data-2.4.2.1\"><span class=\"toc-item-num\">2.4.2.1&nbsp;&nbsp;</span>Plot the Data</a></span></li></ul></li><li><span><a href=\"#Rolling-Window\" data-toc-modified-id=\"Rolling-Window-2.4.3\"><span class=\"toc-item-num\">2.4.3&nbsp;&nbsp;</span>Rolling Window</a></span></li></ul></li><li><span><a href=\"#Time-Zones\" data-toc-modified-id=\"Time-Zones-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Time Zones</a></span><ul class=\"toc-item\"><li><span><a href=\"#Localization-and-Conversion\" data-toc-modified-id=\"Localization-and-Conversion-2.5.1\"><span class=\"toc-item-num\">2.5.1&nbsp;&nbsp;</span>Localization and Conversion</a></span></li><li><span><a href=\"#Operating-between-TIme-Zones\" data-toc-modified-id=\"Operating-between-TIme-Zones-2.5.2\"><span class=\"toc-item-num\">2.5.2&nbsp;&nbsp;</span>Operating between TIme Zones</a></span></li><li><span><a href=\"#Common-Time-Zones\" data-toc-modified-id=\"Common-Time-Zones-2.5.3\"><span class=\"toc-item-num\">2.5.3&nbsp;&nbsp;</span>Common Time Zones</a></span></li></ul></li></ul></li><li><span><a href=\"#Common-Use-Cases\" data-toc-modified-id=\"Common-Use-Cases-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Common Use Cases</a></span><ul class=\"toc-item\"><li><span><a href=\"#Import-data-with-date/time\" data-toc-modified-id=\"Import-data-with-date/time-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Import data with date/time</a></span></li><li><span><a href=\"#Split-into-multiple-columns\" data-toc-modified-id=\"Split-into-multiple-columns-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Split into multiple columns</a></span></li><li><span><a href=\"#Combine-columns-with-Date/Time-information\" data-toc-modified-id=\"Combine-columns-with-Date/Time-information-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Combine columns with Date/Time information</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Conclusion</a></span></li><li><span><a href=\"#References\" data-toc-modified-id=\"References-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>References</a></span></li></ul></div>"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["In the previous notebook, we learned how to be more productive with __[Pandas](https://pandas.pydata.org/)__ by using sophisticated multi-level indexing, aggregating and combining data. In this notebook, we will explore the capabilities for working with Time Series data. We will start with the default `datetime` object in Python and then jump to data structures for working with time series data in Pandas. Let's dive into the details."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["# Import libraries\n", "import pandas as pd\n", "import numpy as np"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["__Time Series__ are one of the most common types of structured data that we encounter in daily life. Stock prices, weather data, energy usage, and even digital health, are all examples of data that can be collected at different time intervals. __Pandas__ was developed in the context of financial modeling, so it contains an extensive set of tools for working with dates, times, and time-indexed data. Date and Time data comes in various flavors such as:\n", "- __Timestamps:__ for specific instants in time such as November 5th, 2020 at 7:00am\n", "- __Fixed periods:__ such as the month November 2010 or the full year 2020\n", "- __Intervals of time:__ length of time between a particular beginning and end point\n", "- __Time deltas:__ reference an exact length of time such as a duration of 30.5 seconds\n", "\n", "In this notebook, we will briefly introduce date and time data types in native python and then focus on how to work with date/time data in Pandas."]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Date and Time in Python\n", "\n", "Python's basic objects for working with time series data reside in the __`datetime`__ module. Let's look at some examples."]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["# Import datetime\n", "from datetime import datetime"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["### Building `datetime` object\n", "\n", "`datetime` objects can be used to quickly perform a host of useful functionalities. A date can be built in various ways and then properties of a `datetime` object can be used to get specific date and time details from it."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.datetime(2020, 11, 25, 13, 23, 22, 106620)"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["# Build a date\n", "now = datetime.now()\n", "now"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`datetime.now()` creates a `datetime` object with current date and time down to the microsecond. "]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Year:  2020\n", "Month:  11\n", "Day:  25\n", "Hour:  13\n", "Minutes:  23\n", "Seconds:  22\n", "Microsecond:  106620\n"]}], "source": ["# Get date and time details from current date\n", "print('Year: ', now.year)\n", "print('Month: ', now.month)\n", "print('Day: ', now.day)\n", "print('Hour: ', now.hour)\n", "print('Minutes: ', now.minute)\n", "print('Seconds: ', now.second)\n", "print('Microsecond: ', now.microsecond)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["A `datetime` object can also be created by specifying year, month, day, and other details."]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.datetime(2020, 9, 10, 0, 0)"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["datetime(year=2020, month=9, day=10)"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.datetime(2020, 9, 10, 11, 30)"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["datetime(year=2020, month=9, day=10, hour=11, minute=30)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Converting between String and DateTime\n", "`strftime` and `strptime` methods can be used to format `datetime` objects and pandas `Timestamp` objects (discussed later in this section).\n", "- `strftime` - convert object to a string according to a given format\n", "- `strptime` - parse a string into a `datetime` object given a corresponding format"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Using `strftime` and `strptime`"]}, {"cell_type": "markdown", "metadata": {}, "source": ["__`strftime`__ can be used to convert a `datetime` object to a string according to a given format. Standard string format codes for printing dates can read about in the [strftime](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) section of Python's [datetime](https://docs.python.org/3/library/datetime.html) documentation."]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.datetime(2020, 11, 25, 13, 23, 22, 106620)"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["# print data\n", "now"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["2020-11-25\n", "2020-11-25\n", "11/25/20\n", "November 25, 2020\n", "25 Nov, 2020\n", "Wednesday\n"]}], "source": ["# apply strftime\n", "\n", "print(now.strftime('%Y-%m-%d'))\n", "print(now.strftime('%F'))\n", "print(now.strftime('%D'))\n", "print(now.strftime('%B %d, %Y'))\n", "print(now.strftime('%d %b, %Y'))\n", "print(now.strftime('%A'))"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'str'>\n"]}], "source": ["# Check type\n", "print(type(now.strftime('%Y-%m-%d')))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Similarly, __`strptime`__ can be used to parse a string into a `datetime` object."]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.datetime(2020, 12, 25, 0, 0)"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["xmas_day = '2020-12-25'\n", "datetime.strptime(xmas_day, '%Y-%m-%d')"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime.datetime(2020, 4, 22, 20, 34, 48)"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["random_day = '20200422T203448'\n", "datetime.strptime(random_day, '%Y%m%dT%H%M%S')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["While `datetime.strptime` is a good way to parse a date when a format is known, it can be annoying to write a format each time."]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Using `parser.parse`\n", "`dateutil` module provides the `parser.parse` method that can parse dates from a variety of string formats."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": ["# Import parse\n", "from dateutil.parser import parse"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["2020-12-25 00:00:00\n", "2015-07-04 00:00:00\n", "2020-11-05 22:45:00\n", "2020-04-22 20:34:48\n"]}], "source": ["# Parse various strings\n", "xmas_day = '2020-12-25'\n", "ind_day = '4th of July, 2015'\n", "random_day = 'Nov 05, 2020 10:45 PM'\n", "random_day2 = '20200422T203448'\n", "\n", "print(parse(xmas_day))\n", "print(parse(ind_day))\n", "print(parse(random_day))\n", "print(parse(random_day2))"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'datetime.datetime'>\n"]}], "source": ["# Check type\n", "print(type(parse(xmas_day)))"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Date and Time in Pandas\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["__Pandas__ provides the following fundamental data structures for working with time series data:\n", "\n", "- `Timestamp` type for working with time stamps. A replacement for Python's native `datetime`, it is based on the more efficient numpy.datetime64 data type. The associated Index structure is `DatetimeIndex`\n", "- `Period` type for working with time Periods. The associated index structure is `PeriodIndex`\n", "- `Timedelta` type for working with time deltas or durations. The associated index structure is `TimedeltaIndex`"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### The Basics"]}, {"cell_type": "markdown", "metadata": {}, "source": ["__Pandas__ provides a __`Timestamp`__ object, which combines the ease of `datetime` and `dateutil` with the efficient storage of `numpy.datetime64`. The __`to_datetime`__ method parses many different kinds of date representations returning a `Timestamp` object. \n", "\n", "Passing a single date to `to_datetime` returns a `Timestamp`."]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/plain": ["Timestamp('2020-07-04 00:00:00')"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create timestamp object\n", "ind_day = pd.to_datetime('4th of July, 2020')\n", "ind_day"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`strftime` can be used to convert this object to a string according to a given format."]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"text/plain": ["'July'"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["# Get full name of Month using strftime\n", "ind_day.strftime('%B')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### `DatetimeIndex`\n", "Passing a series of dates by default returns a __`DatetimeIndex`__ which can be used to index data in a `Series` or `DataFrame`."]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-12-25 00:00:00', '2020-07-04 00:00:00',\n", "               '2018-10-06 00:00:00', '2017-07-07 00:00:00',\n", "               '2020-05-08 00:00:00', '2020-04-22 20:34:48'],\n", "              dtype='datetime64[ns]', freq=None)"]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}], "source": ["dates = pd.to_datetime([datetime(2020, 12, 25), '4th of July, 2020',\n", "                       '2018-Oct-6', '07-07-2017', '20200508', '20200422T203448'])\n", "dates"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["`DatetimeIndex` objects do not have a frequency (hourly, daily, monthly etc.) by default, as they are just snapshots in time. As a result, arithmetic operations such as addition, subtraction, or multiplication cannot be performed directly."]}, {"cell_type": "markdown", "metadata": {}, "source": ["Pandas also supports converting integer or float __[epoch times](https://en.wikipedia.org/wiki/Unix_time)__ to `Timestamp` and `DatetimeIndex`. The default unit is nanoseconds, since that is how `Timestamp` objects are stored internally."]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/plain": ["Timestamp('1970-01-01 00:00:01.349720105')"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create Timestamp\n", "pd.to_datetime(1349720105)"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2012-10-08 18:15:05', '2012-10-09 18:15:05',\n", "               '2012-10-10 18:15:05', '2012-10-11 18:15:05',\n", "               '2012-10-12 18:15:05'],\n", "              dtype='datetime64[ns]', freq=None)"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create DatetimeIndex with seconds\n", "pd.to_datetime([1349720105, 1349806505, 1349892905,\n", "               1349979305, 1350065705], unit='s')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Notice that the date values change based on the unit specified."]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['1970-01-16 14:55:20.105000', '1970-01-16 14:56:46.505000',\n", "               '1970-01-16 14:58:12.905000', '1970-01-16 14:59:39.305000',\n", "               '1970-01-16 15:01:05.705000'],\n", "              dtype='datetime64[ns]', freq=None)"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create DatetimeIndex with milliseconds\n", "pd.to_datetime([1349720105, 1349806505, 1349892905,\n", "               1349979305, 1350065705], unit='ms')"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['1970-01-01 00:00:01.349720105',\n", "               '1970-01-01 00:00:01.349806505',\n", "               '1970-01-01 00:00:01.349892905',\n", "               '1970-01-01 00:00:01.349979305',\n", "               '1970-01-01 00:00:01.350065705'],\n", "              dtype='datetime64[ns]', freq=None)"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.to_datetime([1349720105, 1349806505, 1349892905,\n", "               1349979305, 1350065705], unit='ns')"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["#### `PeriodIndex`\n", "A Timestamp represents a point in time, whereas a Period represents an interval in time. Time Periods can be used to check if a specific event occurs within a certain period, such as when monitoring the number of flights taking off or the average stock price during a period.\n", "\n", "A `DatetimeIndex` object can be converted to a __`PeriodIndex`__ using the __`to_period()`__ function by specifying a frequency (such as `D` to indicate daily frequency)."]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2020-12-25', '2020-07-04', '2018-10-06', '2017-07-07',\n", "             '2020-05-08', '2020-04-22'],\n", "            dtype='period[D]', freq='D')"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create daily time periods\n", "period_daily = dates.to_period('D')\n", "period_daily"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Since a period represents a time interval, it has a `start_time` and an `end_time`."]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-12-25', '2020-07-04', '2018-10-06', '2017-07-07',\n", "               '2020-05-08', '2020-04-22'],\n", "              dtype='datetime64[ns]', freq=None)"]}, "execution_count": 24, "metadata": {}, "output_type": "execute_result"}], "source": ["# Start time of a Period\n", "period_daily.start_time"]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-12-25 23:59:59.999999999',\n", "               '2020-07-04 23:59:59.999999999',\n", "               '2018-10-06 23:59:59.999999999',\n", "               '2017-07-07 23:59:59.999999999',\n", "               '2020-05-08 23:59:59.999999999',\n", "               '2020-04-22 23:59:59.999999999'],\n", "              dtype='datetime64[ns]', freq=None)"]}, "execution_count": 25, "metadata": {}, "output_type": "execute_result"}], "source": ["# End time of a Period\n", "period_daily.end_time"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Note that the `start_time` and `end_time` are `DatetimeIndex` objects because the start and end times are just a snapshot in time of the time period.\n", "\n", "To reiterate the concept, let's look at another example."]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Period is:  2020-12-25\n", "Timestamp is:  2020-12-25 18:12:00\n"]}, {"data": {"text/plain": ["True"]}, "execution_count": 26, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create time period\n", "p1 = pd.Period('2020-12-25')\n", "print('Period is: ', p1)\n", "\n", "# Create time stamp\n", "t1 = pd.Timestamp('2020-12-25 18:12')\n", "print('Timestamp is: ', t1)\n", "\n", "# Test Time interval\n", "p1.start_time < t1 < p1.end_time"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Since `Period` is an interval of time, the test returns `True` showing that `Timestamp` lies within the time interval."]}, {"cell_type": "markdown", "metadata": {}, "source": ["##### Arithmetic Operations\n", "Now that a frequency is associated with the object, various __arithmetic operations__ can be performed."]}, {"cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2020-11-25', '2020-06-04', '2018-09-06', '2017-06-07',\n", "             '2020-04-08', '2020-03-23'],\n", "            dtype='period[D]', freq='D')"]}, "execution_count": 27, "metadata": {}, "output_type": "execute_result"}], "source": ["# Subtract 30 days \n", "period_daily - 30"]}, {"cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2021-01-04', '2020-07-14', '2018-10-16', '2017-07-17',\n", "             '2020-05-18', '2020-05-02'],\n", "            dtype='period[D]', freq='D')"]}, "execution_count": 28, "metadata": {}, "output_type": "execute_result"}], "source": ["# Add 10 days\n", "period_daily + 10"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Similarly, we can create time periods with monthly frequency and perform arithmetic operations."]}, {"cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2020-12', '2020-07', '2018-10', '2017-07', '2020-05', '2020-04'], dtype='period[M]', freq='M')"]}, "execution_count": 29, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create monthly frequency\n", "period_monthly = dates.to_period('M')\n", "period_monthly"]}, {"cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2019-12', '2019-07', '2017-10', '2016-07', '2019-05', '2019-04'], dtype='period[M]', freq='M')"]}, "execution_count": 30, "metadata": {}, "output_type": "execute_result"}], "source": ["# Subtract 12 months\n", "period_monthly - 12"]}, {"cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2021-10', '2021-05', '2019-08', '2018-05', '2021-03', '2021-02'], dtype='period[M]', freq='M')"]}, "execution_count": 31, "metadata": {}, "output_type": "execute_result"}], "source": ["# Add 10 months\n", "period_monthly + 10"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### `TimedeltaIndex`\n", "Time deltas represent the temporal difference between two `datetime` objects. Time deltas come in handy when you need to calculate the difference between two dates. A __`TimedeltaIndex`__ can be easily created by subtracting a date from `dates`."]}, {"cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [{"data": {"text/plain": ["TimedeltaIndex([   '224 days 00:00:00',     '50 days 00:00:00',\n", "                 '-587 days +00:00:00', '-1043 days +00:00:00',\n", "                   '-7 days +00:00:00',   '-23 days +20:34:48'],\n", "               dtype='timedelta64[ns]', freq=None)"]}, "execution_count": 32, "metadata": {}, "output_type": "execute_result"}], "source": ["# Subtract a specific date from dates\n", "dates - pd.to_datetime('2020-05-15')"]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [{"data": {"text/plain": ["TimedeltaIndex(['1267 days 00:00:00', '1093 days 00:00:00',\n", "                 '456 days 00:00:00',    '0 days 00:00:00',\n", "                '1036 days 00:00:00', '1020 days 20:34:48'],\n", "               dtype='timedelta64[ns]', freq=None)"]}, "execution_count": 33, "metadata": {}, "output_type": "execute_result"}], "source": ["# Subtract date using index\n", "dates - dates[3]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Date Range and Frequency\n", "\n", "Regular date sequences can be created using functions, such as `pd.date_range()` for timestamps, `pd.period_range()` for periods, and `pd.timedelta_range()` for time deltas. For many applications, this is sufficient. Fixed frequency, such as daily, monthly, or every 15 minutes, are often desirable. __Pandas__ provides a full suite of standard time series frequencies found [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects)."]}, {"cell_type": "markdown", "metadata": {}, "source": ["- __Create a Sequence of Dates__ - by default, the frequency is daily. Both the start and end dates are included in the result."]}, {"cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-08-03', '2020-08-04', '2020-08-05', '2020-08-06',\n", "               '2020-08-07', '2020-08-08', '2020-08-09', '2020-08-10'],\n", "              dtype='datetime64[ns]', freq='D')"]}, "execution_count": 34, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.date_range('2020-08-03', '2020-08-10')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- __Sequence of Dates with Period__ - alternatively, a date range can be specified with a startpoint and a number of periods."]}, {"cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-08-03', '2020-08-04', '2020-08-05', '2020-08-06',\n", "               '2020-08-07', '2020-08-08', '2020-08-09', '2020-08-10'],\n", "              dtype='datetime64[ns]', freq='D')"]}, "execution_count": 35, "metadata": {}, "output_type": "execute_result"}], "source": ["dt_rng = pd.date_range('2020-08-03', periods=8)\n", "dt_rng"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Note that the output when using `date_range()` is a `DatetimeIndex` object where each date is a snapshot in time (`Timestamp`)."]}, {"cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [{"data": {"text/plain": ["Timestamp('2020-08-03 00:00:00', freq='D')"]}, "execution_count": 36, "metadata": {}, "output_type": "execute_result"}], "source": ["dt_rng[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- __Changing the Frequency__ - the frequency can be modified by altering the `freq` argument. __Pandas__ provides a full suite of standard time series frequencies found [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects)."]}, {"cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-08-03 00:00:00', '2020-08-03 01:00:00',\n", "               '2020-08-03 02:00:00', '2020-08-03 03:00:00',\n", "               '2020-08-03 04:00:00', '2020-08-03 05:00:00',\n", "               '2020-08-03 06:00:00', '2020-08-03 07:00:00'],\n", "              dtype='datetime64[ns]', freq='H')"]}, "execution_count": 37, "metadata": {}, "output_type": "execute_result"}], "source": ["# Date range with Hourly Frequency\n", "pd.date_range('2020-08-03', periods=8, freq='H')"]}, {"cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01',\n", "               '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01'],\n", "              dtype='datetime64[ns]', freq='MS')"]}, "execution_count": 38, "metadata": {}, "output_type": "execute_result"}], "source": ["# Date range with Month Start Frequency\n", "pd.date_range('2020-02-03', periods=8, freq='MS')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- __Create a Sequence of Periods__"]}, {"cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2020-02', '2020-03', '2020-04', '2020-05', '2020-06', '2020-07',\n", "             '2020-08', '2020-09'],\n", "            dtype='period[M]', freq='M')"]}, "execution_count": 39, "metadata": {}, "output_type": "execute_result"}], "source": ["# Period Range with Monthly Frequency\n", "pd.period_range('2020-02-03', periods=8, freq='M')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["`pd.period_range()` generated eight periods with monthly frequency. Note that the output is a `PeriodIndex` object. As mentioned earlier, `Period` represents an interval in time, whereas `Timestamp` represents a point in time."]}, {"cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [{"data": {"text/plain": ["PeriodIndex(['2020-02-03', '2020-02-04', '2020-02-05', '2020-02-06',\n", "             '2020-02-07', '2020-02-08', '2020-02-09', '2020-02-10'],\n", "            dtype='period[D]', freq='D')"]}, "execution_count": 40, "metadata": {}, "output_type": "execute_result"}], "source": ["prd_rng = pd.period_range('2020-02-03', periods=8, freq='D')\n", "prd_rng"]}, {"cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Start time for period at 0 index:  2020-02-03 00:00:00\n", "End time for period at 0 index:  2020-02-03 23:59:59.999999999\n"]}], "source": ["# Print start and end times\n", "print('Start time for period at 0 index: ', prd_rng[0].start_time)\n", "print('End time for period at 0 index: ', prd_rng[0].end_time)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- __Create a Sequence of Durations (Time Deltas)__"]}, {"cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [{"data": {"text/plain": ["TimedeltaIndex(['1 days', '2 days', '3 days', '4 days', '5 days', '6 days'], dtype='timedelta64[ns]', freq='D')"]}, "execution_count": 42, "metadata": {}, "output_type": "execute_result"}], "source": ["# Time Deltas with daily frequency\n", "pd.timedelta_range(start='1 day', periods=6)"]}, {"cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [{"data": {"text/plain": ["TimedeltaIndex(['00:00:00', '01:00:00', '02:00:00', '03:00:00', '04:00:00',\n", "                '05:00:00', '06:00:00', '07:00:00'],\n", "               dtype='timedelta64[ns]', freq='H')"]}, "execution_count": 43, "metadata": {}, "output_type": "execute_result"}], "source": ["# Time deltas with hourly frequency\n", "pd.timedelta_range(0, periods=8, freq='H')"]}, {"cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [{"data": {"text/plain": ["TimedeltaIndex(['1 days 00:00:00', '1 days 06:00:00', '1 days 12:00:00',\n", "                '1 days 18:00:00', '2 days 00:00:00', '2 days 06:00:00',\n", "                '2 days 12:00:00', '2 days 18:00:00', '3 days 00:00:00'],\n", "               dtype='timedelta64[ns]', freq='6H')"]}, "execution_count": 44, "metadata": {}, "output_type": "execute_result"}], "source": ["# Time deltas with a 6 hour frequency\n", "pd.timedelta_range(start='1 day', end='3 days', freq='6H')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Combining Frequency Codes\n", "__Frequency codes__ can also be combined with numbers to specify other frequencies. For example, a frequency of 1 hour and 30 minutes can be created by combining the hour `H` and minute `T` codes."]}, {"cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2020-08-03 00:00:00', '2020-08-03 01:30:00',\n", "               '2020-08-03 03:00:00', '2020-08-03 04:30:00',\n", "               '2020-08-03 06:00:00', '2020-08-03 07:30:00',\n", "               '2020-08-03 09:00:00', '2020-08-03 10:30:00',\n", "               '2020-08-03 12:00:00', '2020-08-03 13:30:00'],\n", "              dtype='datetime64[ns]', freq='90T')"]}, "execution_count": 45, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.date_range('2020-08-03', periods=10, freq='1H30T')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Similarly, a frequency of 1 day 5 hours and 30 mins can be created by combining the day `D`, hour `H` and minute `T` codes. As an example, we will create a `timedelta_range`."]}, {"cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [{"data": {"text/plain": ["TimedeltaIndex([ '0 days 00:00:00',  '1 days 05:30:00',  '2 days 11:00:00',\n", "                 '3 days 16:30:00',  '4 days 22:00:00',  '6 days 03:30:00',\n", "                 '7 days 09:00:00',  '8 days 14:30:00',  '9 days 20:00:00',\n", "                '11 days 01:30:00'],\n", "               dtype='timedelta64[ns]', freq='1770T')"]}, "execution_count": 46, "metadata": {}, "output_type": "execute_result"}], "source": ["pd.timedelta_range(0, periods=10, freq='1D5H30T')"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["### Indexing and Selection\n", "\n", "Pandas time series tools provide the ability to use dates and times as indices to organize data. This allows for the benefits of indexed data, such as automatic alignment, data slicing, and selection etc.\n", "\n", "Pandas was developed with a financial context, so it includes some very specific tools for financial data. The `pandas-datareader` package (installable via `conda install pandas-datareader`) can import financial data from a number of available sources. Here, we will load [stock price data for GE](https://finance.yahoo.com/quote/GE/) as an example."]}, {"cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>High</th>\n", "      <th>Low</th>\n", "      <th>Open</th>\n", "      <th>Close</th>\n", "      <th>Volume</th>\n", "      <th>Adj Close</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-04</th>\n", "      <td>15.038462</td>\n", "      <td>14.567307</td>\n", "      <td>14.634615</td>\n", "      <td>14.855769</td>\n", "      <td>69763000.0</td>\n", "      <td>10.840267</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-05</th>\n", "      <td>15.067307</td>\n", "      <td>14.855769</td>\n", "      <td>14.865385</td>\n", "      <td>14.932693</td>\n", "      <td>67132600.0</td>\n", "      <td>10.896401</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-06</th>\n", "      <td>15.019231</td>\n", "      <td>14.846154</td>\n", "      <td>14.932693</td>\n", "      <td>14.855769</td>\n", "      <td>57683400.0</td>\n", "      <td>10.840267</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-07</th>\n", "      <td>15.846154</td>\n", "      <td>14.836538</td>\n", "      <td>14.884615</td>\n", "      <td>15.625000</td>\n", "      <td>192891100.0</td>\n", "      <td>11.401575</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-08</th>\n", "      <td>16.048077</td>\n", "      <td>15.644231</td>\n", "      <td>15.682693</td>\n", "      <td>15.961538</td>\n", "      <td>119717100.0</td>\n", "      <td>11.647147</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                 High        Low       Open      Close       Volume  Adj Close\n", "Date                                                                          \n", "2010-01-04  15.038462  14.567307  14.634615  14.855769   69763000.0  10.840267\n", "2010-01-05  15.067307  14.855769  14.865385  14.932693   67132600.0  10.896401\n", "2010-01-06  15.019231  14.846154  14.932693  14.855769   57683400.0  10.840267\n", "2010-01-07  15.846154  14.836538  14.884615  15.625000  192891100.0  11.401575\n", "2010-01-08  16.048077  15.644231  15.682693  15.961538  119717100.0  11.647147"]}, "execution_count": 47, "metadata": {}, "output_type": "execute_result"}], "source": ["# Get stock data\n", "from pandas_datareader import data\n", "\n", "ge = data.DataReader('GE', start='2010', end='2021',\n", "                       data_source='yahoo')\n", "ge.head()"]}, {"cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [{"data": {"text/plain": ["dtype('<M8[ns]')"]}, "execution_count": 48, "metadata": {}, "output_type": "execute_result"}], "source": ["# Check data type\n", "ge.index.dtype"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Pandas stores timestamps using NumPy\u2019s `datetime64` data type at the nanosecond level. Scalar values from a `DatetimeIndex` are pandas `Timestamp` objects."]}, {"cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [{"data": {"text/plain": ["Timestamp('2010-01-04 00:00:00')"]}, "execution_count": 49, "metadata": {}, "output_type": "execute_result"}], "source": ["# Check data type\n", "ge.index[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- Select Data for Specific Date"]}, {"cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [{"data": {"text/plain": ["High         2.561539e+01\n", "Low          2.519231e+01\n", "Open         2.550961e+01\n", "Close        2.529808e+01\n", "Volume       2.897240e+07\n", "Adj Close    2.216700e+01\n", "Name: 2015-07-06 00:00:00, dtype: float64"]}, "execution_count": 50, "metadata": {}, "output_type": "execute_result"}], "source": ["ge.loc['2015-07-06',:]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- Dates can be specified in different formats as follows:"]}, {"cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["High         2.561539e+01\n", "Low          2.519231e+01\n", "Open         2.550961e+01\n", "Close        2.529808e+01\n", "Volume       2.897240e+07\n", "Adj Close    2.216700e+01\n", "Name: 2015-07-06 00:00:00, dtype: float64\n", "High         2.561539e+01\n", "Low          2.519231e+01\n", "Open         2.550961e+01\n", "Close        2.529808e+01\n", "Volume       2.897240e+07\n", "Adj Close    2.216700e+01\n", "Name: 2015-07-06 00:00:00, dtype: float64\n", "High         2.561539e+01\n", "Low          2.519231e+01\n", "Open         2.550961e+01\n", "Close        2.529808e+01\n", "Volume       2.897240e+07\n", "Adj Close    2.216700e+01\n", "Name: 2015-07-06 00:00:00, dtype: float64\n"]}], "source": ["print(ge.loc['07/06/2015',:])\n", "print(ge.loc['20150706',:])\n", "print(ge.loc[datetime(2015, 7, 6),:])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- Partial string indexing allows for selection using just the year or month"]}, {"cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>High</th>\n", "      <th>Low</th>\n", "      <th>Open</th>\n", "      <th>Close</th>\n", "      <th>Volume</th>\n", "      <th>Adj Close</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-01-02</th>\n", "      <td>11.96</td>\n", "      <td>11.23</td>\n", "      <td>11.23</td>\n", "      <td>11.93</td>\n", "      <td>87421800.0</td>\n", "      <td>11.880686</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-03</th>\n", "      <td>12.00</td>\n", "      <td>11.53</td>\n", "      <td>11.57</td>\n", "      <td>11.97</td>\n", "      <td>85885800.0</td>\n", "      <td>11.920521</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-06</th>\n", "      <td>12.21</td>\n", "      <td>11.84</td>\n", "      <td>11.84</td>\n", "      <td>12.14</td>\n", "      <td>111948700.0</td>\n", "      <td>12.089818</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-07</th>\n", "      <td>12.24</td>\n", "      <td>11.92</td>\n", "      <td>12.15</td>\n", "      <td>12.05</td>\n", "      <td>70579300.0</td>\n", "      <td>12.000189</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-08</th>\n", "      <td>12.05</td>\n", "      <td>11.87</td>\n", "      <td>11.99</td>\n", "      <td>11.94</td>\n", "      <td>55402500.0</td>\n", "      <td>11.890644</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-11-19</th>\n", "      <td>9.76</td>\n", "      <td>9.51</td>\n", "      <td>9.62</td>\n", "      <td>9.66</td>\n", "      <td>87177500.0</td>\n", "      <td>9.660000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-11-20</th>\n", "      <td>9.83</td>\n", "      <td>9.59</td>\n", "      <td>9.64</td>\n", "      <td>9.76</td>\n", "      <td>79923400.0</td>\n", "      <td>9.760000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-11-23</th>\n", "      <td>10.27</td>\n", "      <td>9.86</td>\n", "      <td>9.86</td>\n", "      <td>10.07</td>\n", "      <td>108197300.0</td>\n", "      <td>10.070000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-11-24</th>\n", "      <td>10.85</td>\n", "      <td>10.40</td>\n", "      <td>10.71</td>\n", "      <td>10.45</td>\n", "      <td>175891500.0</td>\n", "      <td>10.450000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-11-25</th>\n", "      <td>10.56</td>\n", "      <td>10.34</td>\n", "      <td>10.53</td>\n", "      <td>10.50</td>\n", "      <td>107645396.0</td>\n", "      <td>10.500000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>229 rows \u00d7 6 columns</p>\n", "</div>"], "text/plain": ["             High    Low   Open  Close       Volume  Adj Close\n", "Date                                                          \n", "2020-01-02  11.96  11.23  11.23  11.93   87421800.0  11.880686\n", "2020-01-03  12.00  11.53  11.57  11.97   85885800.0  11.920521\n", "2020-01-06  12.21  11.84  11.84  12.14  111948700.0  12.089818\n", "2020-01-07  12.24  11.92  12.15  12.05   70579300.0  12.000189\n", "2020-01-08  12.05  11.87  11.99  11.94   55402500.0  11.890644\n", "...           ...    ...    ...    ...          ...        ...\n", "2020-11-19   9.76   9.51   9.62   9.66   87177500.0   9.660000\n", "2020-11-20   9.83   9.59   9.64   9.76   79923400.0   9.760000\n", "2020-11-23  10.27   9.86   9.86  10.07  108197300.0  10.070000\n", "2020-11-24  10.85  10.40  10.71  10.45  175891500.0  10.450000\n", "2020-11-25  10.56  10.34  10.53  10.50  107645396.0  10.500000\n", "\n", "[229 rows x 6 columns]"]}, "execution_count": 52, "metadata": {}, "output_type": "execute_result"}], "source": ["# Using year\n", "ge.loc['2020']"]}, {"cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>High</th>\n", "      <th>Low</th>\n", "      <th>Open</th>\n", "      <th>Close</th>\n", "      <th>Volume</th>\n", "      <th>Adj Close</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-10-01</th>\n", "      <td>6.29</td>\n", "      <td>6.11</td>\n", "      <td>6.27</td>\n", "      <td>6.24</td>\n", "      <td>79175600.0</td>\n", "      <td>6.24</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-02</th>\n", "      <td>6.40</td>\n", "      <td>6.05</td>\n", "      <td>6.05</td>\n", "      <td>6.39</td>\n", "      <td>90076400.0</td>\n", "      <td>6.39</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-05</th>\n", "      <td>6.45</td>\n", "      <td>6.32</td>\n", "      <td>6.39</td>\n", "      <td>6.41</td>\n", "      <td>58283600.0</td>\n", "      <td>6.41</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-06</th>\n", "      <td>6.58</td>\n", "      <td>6.11</td>\n", "      <td>6.43</td>\n", "      <td>6.17</td>\n", "      <td>170066200.0</td>\n", "      <td>6.17</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-07</th>\n", "      <td>6.40</td>\n", "      <td>6.21</td>\n", "      <td>6.22</td>\n", "      <td>6.31</td>\n", "      <td>83286100.0</td>\n", "      <td>6.31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-08</th>\n", "      <td>6.67</td>\n", "      <td>6.34</td>\n", "      <td>6.36</td>\n", "      <td>6.65</td>\n", "      <td>103167300.0</td>\n", "      <td>6.65</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-09</th>\n", "      <td>7.07</td>\n", "      <td>6.70</td>\n", "      <td>7.07</td>\n", "      <td>6.84</td>\n", "      <td>171507500.0</td>\n", "      <td>6.84</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-12</th>\n", "      <td>6.92</td>\n", "      <td>6.74</td>\n", "      <td>6.92</td>\n", "      <td>6.83</td>\n", "      <td>89036400.0</td>\n", "      <td>6.83</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-13</th>\n", "      <td>6.82</td>\n", "      <td>6.66</td>\n", "      <td>6.79</td>\n", "      <td>6.72</td>\n", "      <td>75287600.0</td>\n", "      <td>6.72</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-14</th>\n", "      <td>6.89</td>\n", "      <td>6.72</td>\n", "      <td>6.72</td>\n", "      <td>6.82</td>\n", "      <td>98076200.0</td>\n", "      <td>6.82</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-15</th>\n", "      <td>6.88</td>\n", "      <td>6.61</td>\n", "      <td>6.70</td>\n", "      <td>6.87</td>\n", "      <td>89252700.0</td>\n", "      <td>6.87</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-16</th>\n", "      <td>7.35</td>\n", "      <td>6.94</td>\n", "      <td>6.96</td>\n", "      <td>7.29</td>\n", "      <td>169147300.0</td>\n", "      <td>7.29</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-19</th>\n", "      <td>7.47</td>\n", "      <td>7.23</td>\n", "      <td>7.39</td>\n", "      <td>7.29</td>\n", "      <td>130837100.0</td>\n", "      <td>7.29</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-20</th>\n", "      <td>7.42</td>\n", "      <td>7.27</td>\n", "      <td>7.35</td>\n", "      <td>7.34</td>\n", "      <td>98420100.0</td>\n", "      <td>7.34</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-21</th>\n", "      <td>7.41</td>\n", "      <td>7.27</td>\n", "      <td>7.28</td>\n", "      <td>7.32</td>\n", "      <td>73811100.0</td>\n", "      <td>7.32</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-22</th>\n", "      <td>7.75</td>\n", "      <td>7.32</td>\n", "      <td>7.33</td>\n", "      <td>7.72</td>\n", "      <td>95766900.0</td>\n", "      <td>7.72</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-23</th>\n", "      <td>8.03</td>\n", "      <td>7.56</td>\n", "      <td>7.93</td>\n", "      <td>7.63</td>\n", "      <td>132563200.0</td>\n", "      <td>7.63</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-26</th>\n", "      <td>7.56</td>\n", "      <td>7.28</td>\n", "      <td>7.46</td>\n", "      <td>7.38</td>\n", "      <td>104254400.0</td>\n", "      <td>7.38</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-27</th>\n", "      <td>7.40</td>\n", "      <td>7.09</td>\n", "      <td>7.40</td>\n", "      <td>7.10</td>\n", "      <td>98170000.0</td>\n", "      <td>7.10</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-28</th>\n", "      <td>7.86</td>\n", "      <td>7.41</td>\n", "      <td>7.51</td>\n", "      <td>7.42</td>\n", "      <td>253494100.0</td>\n", "      <td>7.42</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-29</th>\n", "      <td>7.74</td>\n", "      <td>7.31</td>\n", "      <td>7.66</td>\n", "      <td>7.37</td>\n", "      <td>123298000.0</td>\n", "      <td>7.37</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-10-30</th>\n", "      <td>7.54</td>\n", "      <td>7.29</td>\n", "      <td>7.34</td>\n", "      <td>7.42</td>\n", "      <td>102370100.0</td>\n", "      <td>7.42</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            High   Low  Open  Close       Volume  Adj Close\n", "Date                                                       \n", "2020-10-01  6.29  6.11  6.27   6.24   79175600.0       6.24\n", "2020-10-02  6.40  6.05  6.05   6.39   90076400.0       6.39\n", "2020-10-05  6.45  6.32  6.39   6.41   58283600.0       6.41\n", "2020-10-06  6.58  6.11  6.43   6.17  170066200.0       6.17\n", "2020-10-07  6.40  6.21  6.22   6.31   83286100.0       6.31\n", "2020-10-08  6.67  6.34  6.36   6.65  103167300.0       6.65\n", "2020-10-09  7.07  6.70  7.07   6.84  171507500.0       6.84\n", "2020-10-12  6.92  6.74  6.92   6.83   89036400.0       6.83\n", "2020-10-13  6.82  6.66  6.79   6.72   75287600.0       6.72\n", "2020-10-14  6.89  6.72  6.72   6.82   98076200.0       6.82\n", "2020-10-15  6.88  6.61  6.70   6.87   89252700.0       6.87\n", "2020-10-16  7.35  6.94  6.96   7.29  169147300.0       7.29\n", "2020-10-19  7.47  7.23  7.39   7.29  130837100.0       7.29\n", "2020-10-20  7.42  7.27  7.35   7.34   98420100.0       7.34\n", "2020-10-21  7.41  7.27  7.28   7.32   73811100.0       7.32\n", "2020-10-22  7.75  7.32  7.33   7.72   95766900.0       7.72\n", "2020-10-23  8.03  7.56  7.93   7.63  132563200.0       7.63\n", "2020-10-26  7.56  7.28  7.46   7.38  104254400.0       7.38\n", "2020-10-27  7.40  7.09  7.40   7.10   98170000.0       7.10\n", "2020-10-28  7.86  7.41  7.51   7.42  253494100.0       7.42\n", "2020-10-29  7.74  7.31  7.66   7.37  123298000.0       7.37\n", "2020-10-30  7.54  7.29  7.34   7.42  102370100.0       7.42"]}, "execution_count": 53, "metadata": {}, "output_type": "execute_result"}], "source": ["# Using year-month\n", "ge.loc['2020-10']"]}, {"cell_type": "markdown", "metadata": {}, "source": ["- Timestamps can be sliced using the `:` notation"]}, {"cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>High</th>\n", "      <th>Low</th>\n", "      <th>Open</th>\n", "      <th>Close</th>\n", "      <th>Volume</th>\n", "      <th>Adj Close</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-05-04</th>\n", "      <td>6.31</td>\n", "      <td>6.15</td>\n", "      <td>6.30</td>\n", "      <td>6.21</td>\n", "      <td>136852400.0</td>\n", "      <td>6.190472</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-05</th>\n", "      <td>6.46</td>\n", "      <td>6.16</td>\n", "      <td>6.28</td>\n", "      <td>6.20</td>\n", "      <td>116998500.0</td>\n", "      <td>6.180502</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-06</th>\n", "      <td>6.25</td>\n", "      <td>5.97</td>\n", "      <td>6.20</td>\n", "      <td>5.98</td>\n", "      <td>117253600.0</td>\n", "      <td>5.961195</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-07</th>\n", "      <td>6.26</td>\n", "      <td>6.06</td>\n", "      <td>6.06</td>\n", "      <td>6.11</td>\n", "      <td>100663300.0</td>\n", "      <td>6.090786</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-08</th>\n", "      <td>6.33</td>\n", "      <td>6.16</td>\n", "      <td>6.21</td>\n", "      <td>6.29</td>\n", "      <td>93934600.0</td>\n", "      <td>6.270220</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-11</th>\n", "      <td>6.25</td>\n", "      <td>6.13</td>\n", "      <td>6.24</td>\n", "      <td>6.19</td>\n", "      <td>71843000.0</td>\n", "      <td>6.170535</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-12</th>\n", "      <td>6.28</td>\n", "      <td>6.00</td>\n", "      <td>6.22</td>\n", "      <td>6.00</td>\n", "      <td>95652200.0</td>\n", "      <td>5.981132</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            High   Low  Open  Close       Volume  Adj Close\n", "Date                                                       \n", "2020-05-04  6.31  6.15  6.30   6.21  136852400.0   6.190472\n", "2020-05-05  6.46  6.16  6.28   6.20  116998500.0   6.180502\n", "2020-05-06  6.25  5.97  6.20   5.98  117253600.0   5.961195\n", "2020-05-07  6.26  6.06  6.06   6.11  100663300.0   6.090786\n", "2020-05-08  6.33  6.16  6.21   6.29   93934600.0   6.270220\n", "2020-05-11  6.25  6.13  6.24   6.19   71843000.0   6.170535\n", "2020-05-12  6.28  6.00  6.22   6.00   95652200.0   5.981132"]}, "execution_count": 54, "metadata": {}, "output_type": "execute_result"}], "source": ["# Slice using Dates\n", "ge.loc['2020-05-04':'2020-05-12']"]}, {"cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>High</th>\n", "      <th>Low</th>\n", "      <th>Open</th>\n", "      <th>Close</th>\n", "      <th>Volume</th>\n", "      <th>Adj Close</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2020-05-01</th>\n", "      <td>6.74</td>\n", "      <td>6.41</td>\n", "      <td>6.67</td>\n", "      <td>6.50</td>\n", "      <td>120376500.0</td>\n", "      <td>6.479559</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-04</th>\n", "      <td>6.31</td>\n", "      <td>6.15</td>\n", "      <td>6.30</td>\n", "      <td>6.21</td>\n", "      <td>136852400.0</td>\n", "      <td>6.190472</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-05</th>\n", "      <td>6.46</td>\n", "      <td>6.16</td>\n", "      <td>6.28</td>\n", "      <td>6.20</td>\n", "      <td>116998500.0</td>\n", "      <td>6.180502</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-06</th>\n", "      <td>6.25</td>\n", "      <td>5.97</td>\n", "      <td>6.20</td>\n", "      <td>5.98</td>\n", "      <td>117253600.0</td>\n", "      <td>5.961195</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-05-07</th>\n", "      <td>6.26</td>\n", "      <td>6.06</td>\n", "      <td>6.06</td>\n", "      <td>6.11</td>\n", "      <td>100663300.0</td>\n", "      <td>6.090786</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-07-27</th>\n", "      <td>6.85</td>\n", "      <td>6.69</td>\n", "      <td>6.84</td>\n", "      <td>6.71</td>\n", "      <td>70704000.0</td>\n", "      <td>6.698927</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-07-28</th>\n", "      <td>6.96</td>\n", "      <td>6.69</td>\n", "      <td>6.70</td>\n", "      <td>6.89</td>\n", "      <td>76033600.0</td>\n", "      <td>6.878630</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-07-29</th>\n", "      <td>7.00</td>\n", "      <td>6.52</td>\n", "      <td>6.99</td>\n", "      <td>6.59</td>\n", "      <td>148442400.0</td>\n", "      <td>6.579125</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-07-30</th>\n", "      <td>6.51</td>\n", "      <td>6.26</td>\n", "      <td>6.50</td>\n", "      <td>6.26</td>\n", "      <td>127526900.0</td>\n", "      <td>6.249670</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-07-31</th>\n", "      <td>6.29</td>\n", "      <td>6.00</td>\n", "      <td>6.25</td>\n", "      <td>6.07</td>\n", "      <td>142731700.0</td>\n", "      <td>6.059984</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>64 rows \u00d7 6 columns</p>\n", "</div>"], "text/plain": ["            High   Low  Open  Close       Volume  Adj Close\n", "Date                                                       \n", "2020-05-01  6.74  6.41  6.67   6.50  120376500.0   6.479559\n", "2020-05-04  6.31  6.15  6.30   6.21  136852400.0   6.190472\n", "2020-05-05  6.46  6.16  6.28   6.20  116998500.0   6.180502\n", "2020-05-06  6.25  5.97  6.20   5.98  117253600.0   5.961195\n", "2020-05-07  6.26  6.06  6.06   6.11  100663300.0   6.090786\n", "...          ...   ...   ...    ...          ...        ...\n", "2020-07-27  6.85  6.69  6.84   6.71   70704000.0   6.698927\n", "2020-07-28  6.96  6.69  6.70   6.89   76033600.0   6.878630\n", "2020-07-29  7.00  6.52  6.99   6.59  148442400.0   6.579125\n", "2020-07-30  6.51  6.26  6.50   6.26  127526900.0   6.249670\n", "2020-07-31  6.29  6.00  6.25   6.07  142731700.0   6.059984\n", "\n", "[64 rows x 6 columns]"]}, "execution_count": 55, "metadata": {}, "output_type": "execute_result"}], "source": ["# Slice using year-month\n", "ge.loc['2020-05':'2020-07']"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Resampling, Shifting, and Windowing"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Resampling\n", "The process of converting a time series from one frequency to another is called __Resampling__. When higher frequency data is aggregated to lower frequency, it is called __downsampling__, while converting lower frequency to higher frequency is called __upsampling__. For simplicity, we'll use just the closing price `Close` data."]}, {"cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    14.932693\n", "2010-01-06    14.855769\n", "2010-01-07    15.625000\n", "2010-01-08    15.961538\n", "Name: Close, dtype: float64"]}, "execution_count": 56, "metadata": {}, "output_type": "execute_result"}], "source": ["ge = ge['Close']\n", "ge.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["__Resampling__ can be done using the `resample()` method, or the much simpler `asfreq()` method. \n", "\n", "- `resample()`: is a data aggregation method. It is a flexible and high-performance method that can be used to process very large time series.\n", "- `asfreq()`: is a data selection method.\n", "\n", "We will __downsample__ the data using 'business year end' frequency `BA` and create a plot of the data returned after applying the two functions."]}, {"cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-12-31    15.893658\n", "2011-12-30    17.434333\n", "2012-12-31    19.453038\n", "2013-12-31    23.083371\n", "2014-12-31    24.994277\n", "2015-12-31    25.767819\n", "2016-12-30    29.180174\n", "2017-12-29    24.972418\n", "2018-12-31    12.402774\n", "2019-12-31     9.782701\n", "2020-12-31     7.963930\n", "Freq: BA-DEC, Name: Close, dtype: float64"]}, "execution_count": 57, "metadata": {}, "output_type": "execute_result"}], "source": ["# Using resample()\n", "ge.resample('BA').mean()"]}, {"cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-12-31    17.586538\n", "2011-12-30    17.221153\n", "2012-12-31    20.182692\n", "2013-12-31    26.951923\n", "2014-12-31    24.298077\n", "2015-12-31    29.951923\n", "2016-12-30    30.384615\n", "2017-12-29    16.778847\n", "2018-12-31     7.278846\n", "2019-12-31    11.160000\n", "Freq: BA-DEC, Name: Close, dtype: float64"]}, "execution_count": 58, "metadata": {}, "output_type": "execute_result"}], "source": ["# Using asfreq()\n", "ge.asfreq('BA')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["##### Downsample Plot\n", "Plot the __down-sampled__ data to compare the returned data of the two functions."]}, {"cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": ["# Import Plotting libraries\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn; seaborn.set()"]}, {"cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1080x720 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Plot data\n", "plt.figure(figsize=(15,10))\n", "ge.plot(alpha=0.5, style='-')\n", "ge.resample('BA').mean().plot(style=':')\n", "ge.asfreq('BA').plot(style='--');\n", "plt.legend(['input', 'resample', 'asfreq'],\n", "           loc='upper left');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["We can see that at each point, `resample` returns the average of the previous year, as shown by the dotted line, while `asfreq` reports the value at the end of the year, as shown by dashed line."]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["__Upsampling__ involves converting from a low frequency to a higher frequency where no aggregation is needed. `resample()` and `asfreq()` are largely equivalent in the case of upsampling. The default for both methods is to leave the up-sampled points empty (filled with NA values). The `asfreq()` method accepts arguments to specify how values are imputed."]}, {"cell_type": "markdown", "metadata": {}, "source": ["We will subset the data and then upsample with daily `D` frequency."]}, {"cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    14.932693\n", "2010-01-06    14.855769\n", "2010-01-07    15.625000\n", "2010-01-08    15.961538\n", "2010-01-11    16.115385\n", "2010-01-12    16.125000\n", "2010-01-13    16.182692\n", "2010-01-14    16.057692\n", "2010-01-15    15.807693\n", "Name: Close, dtype: float64"]}, "execution_count": 61, "metadata": {}, "output_type": "execute_result"}], "source": ["# Subset Data\n", "ge_up_data = ge.iloc[:10]\n", "ge_up_data"]}, {"cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    14.932693\n", "2010-01-06    14.855769\n", "2010-01-07    15.625000\n", "2010-01-08    15.961538\n", "2010-01-09          NaN\n", "2010-01-10          NaN\n", "2010-01-11    16.115385\n", "2010-01-12    16.125000\n", "2010-01-13    16.182692\n", "2010-01-14    16.057692\n", "2010-01-15    15.807693\n", "Freq: D, Name: Close, dtype: float64"]}, "execution_count": 62, "metadata": {}, "output_type": "execute_result"}], "source": ["# Upsample with Daily frequency\n", "ge_up_data.asfreq('D')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The default is to leave the up-sampled points empty (filled with NA values). Forward `ffill` or Backward `bfill` methods can be used to impute missing values."]}, {"cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    14.932693\n", "2010-01-06    14.855769\n", "2010-01-07    15.625000\n", "2010-01-08    15.961538\n", "2010-01-09    15.961538\n", "2010-01-10    15.961538\n", "2010-01-11    16.115385\n", "2010-01-12    16.125000\n", "2010-01-13    16.182692\n", "2010-01-14    16.057692\n", "2010-01-15    15.807693\n", "Freq: D, Name: Close, dtype: float64"]}, "execution_count": 63, "metadata": {}, "output_type": "execute_result"}], "source": ["# Using forward fill\n", "ge_up_data.asfreq('D', method='ffill')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["##### Upsample Plot\n", "Plot the __up-sampled__ data to compare the data returned from various fill methods."]}, {"cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x576 with 2 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(2, figsize=(10,8), sharex=True)\n", "ge_up_data.asfreq('D').plot(ax=ax[0])\n", "\n", "ge_up_data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\n", "ge_up_data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\n", "ax[1].legend([\"back-fill\", \"forward-fill\"]);"]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["The top plot shows upsampled data using a daily frequency with default settings where non-business days are `NA` values that do not appear on the plot. The bottom plot shows forward and backward fill strategies for filling the gaps."]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Shifting\n", "A common use case of time series is shifting of data in time i.e. moving data backward and forward through time. Pandas includes `shift()` and `tshift()` methods for shifting data.\n", "- __`shift()`__ -  shifts the data\n", "- __`tshift()`__ - shifts the index\n", "\n", "In both cases, the shift is specified in multiples of the frequency. Let's look at some examples."]}, {"cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    14.932693\n", "2010-01-06    14.855769\n", "2010-01-07    15.625000\n", "2010-01-08    15.961538\n", "2010-01-11    16.115385\n", "2010-01-12    16.125000\n", "2010-01-13    16.182692\n", "2010-01-14    16.057692\n", "2010-01-15    15.807693\n", "Name: Close, dtype: float64"]}, "execution_count": 65, "metadata": {}, "output_type": "execute_result"}], "source": ["# Print Data\n", "ge_up_data"]}, {"cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04          NaN\n", "2010-01-05          NaN\n", "2010-01-06    14.855769\n", "2010-01-07    14.932693\n", "2010-01-08    14.855769\n", "2010-01-11    15.625000\n", "2010-01-12    15.961538\n", "2010-01-13    16.115385\n", "2010-01-14    16.125000\n", "2010-01-15    16.182692\n", "Name: Close, dtype: float64"]}, "execution_count": 66, "metadata": {}, "output_type": "execute_result"}], "source": ["# Shift Forward\n", "ge_up_data.shift(2)"]}, {"cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    15.625000\n", "2010-01-06    15.961538\n", "2010-01-07    16.115385\n", "2010-01-08    16.125000\n", "2010-01-11    16.182692\n", "2010-01-12    16.057692\n", "2010-01-13    15.807693\n", "2010-01-14          NaN\n", "2010-01-15          NaN\n", "Name: Close, dtype: float64"]}, "execution_count": 67, "metadata": {}, "output_type": "execute_result"}], "source": ["# Shift Backward\n", "ge_up_data.shift(-2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Both _forward_ and _backward_ `shift()` opertions shift the data leaving the index unmodified. Let's look at how index is modified with `tshift()`."]}, {"cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2009-12-31    14.855769\n", "2010-01-01    14.932693\n", "2010-01-04    14.855769\n", "2010-01-05    15.625000\n", "2010-01-06    15.961538\n", "2010-01-07    16.115385\n", "2010-01-08    16.125000\n", "2010-01-11    16.182692\n", "2010-01-12    16.057692\n", "2010-01-13    15.807693\n", "Freq: B, Name: Close, dtype: float64"]}, "execution_count": 68, "metadata": {}, "output_type": "execute_result"}], "source": ["# Shift backward with Index\n", "ge_up_data.tshift(-2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The index for the original data ranges from `2008-01-02 - 2008-01-15`. Using `thsift()` for shifting backward, we see that the index now ranges from `2007-12-31 - 2008-01-11`. Shift takes the same frequency as the frequency of `datetime`."]}, {"cell_type": "markdown", "metadata": {}, "source": ["##### Plot the Data\n", "Let's look at another example of shifting data using `shift()` and `tshift()` to shift the `ge` data. We will plot the data to visualize the differences."]}, {"cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1080x576 with 3 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(3, figsize=(15,8), sharey=True)\n", "\n", "# apply a frequency to the data\n", "ge = ge.asfreq('D', method='pad')\n", "\n", "# shift the data\n", "ge.plot(ax=ax[0])\n", "ge.shift(900).plot(ax=ax[1])\n", "ge.tshift(900).plot(ax=ax[2])\n", "\n", "# legends and annotations\n", "local_max = pd.to_datetime('2012-05-05')\n", "offset = pd.Timedelta(900, 'D')\n", "\n", "ax[0].legend(['input'], loc=2)\n", "ax[0].get_xticklabels()[2].set(weight='heavy', color='red')\n", "ax[0].axvline(local_max, alpha=0.3, color='red')\n", "\n", "ax[1].legend(['shift(900)'], loc=2)\n", "ax[1].get_xticklabels()[2].set(weight='heavy', color='red')\n", "ax[1].axvline(local_max + offset, alpha=0.3, color='red')\n", "\n", "ax[2].legend(['tshift(900)'], loc=2)\n", "ax[2].get_xticklabels()[1].set(weight='heavy', color='red')\n", "ax[2].axvline(local_max + offset, alpha=0.3, color='red');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The top panel in the plot shows `ge` data with a <span style=\"color:red\">red line</span> showing a local date. The middle panel shows the `shift(900)` operation which shifts the data by 900 days, leaving `NA` values at early indices. This is represented by the fact that there is no line on the plot for first 900 days. The bottom panel shows the `tshift(900)` operation, which shifts the index by 900 days, changing the start and end date ranges as shown."]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["#### Rolling Window\n", "Rolling statistics are another time series specific operation where data is evaluated over a sliding window. Rolling operations are useful for smoothing noisy data. The `rolling()` operator behaves similarly to `resample` and `groupby` operations, but instead of grouping, it enables grouping over a sliding window."]}, {"cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [{"data": {"image/png": 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aMhPAP5fvjxmktXlVZKTZQoFZPOvRTNOE2dEIvfYg9NoDMOqP4OzGJjQ4xmB16ySYnhaoO9+HPHoG5AkLII+aAjjSIAjWuW0T5sM2YT7W76zCP97ciu/cfg6KC7JC53fMvhKO2Vf2OIbSogwAwJ4jTbgmbHswQItFsLvguuIBeN//PXzv/RZCZiFsZyyCbcoSiM6MXr9vIqJUYJBGREQ0iPZ2KcgBANnpVkRmC1RmVHUDDkhR+3Xl8Wuh1x9tPt7Dnp3CA672sP5sQZsr61Db6EVWuj0sSIsO/kzDsKonGhqk/FJA9aHj798CYAKiDDFvNBRbJpoNq2+bWDge6Z//TWiKYXdUQ0KrmQabLY4otRv7j7dg9+FGTBmbG7UmzjBNiIIARdUhSyJEUYCUWwL39T+GdmA91L2roGx6DbaJ51rfp65BkPj4RET9i79liIiIBkjX2Y4XzB4VM5gqyrUCGbvNCqB6WzMW5PVpve/UhS2sKXV4efyWdj88fg2//78dAIC8LCeCs/7CG1mbmgJly+tQdn0AKF5Io6cj7ZL7INhdcJ7/FYjZxRDzxkCQZKxafxQ7j+wHAGsKYS8BGmBN9QSSWwMXrqquA1PG5qK+xQvAakng9etQVB2GAdz7vysxcUw27r95tjU+UYKtfCFs5QtheFvxyf4OvLp6LR4eswai3QXHwlsgunP6NCYiou4wSCMiIhogmt4ZbH3p0klId9oigrTxIzMxY3weFs8YCQChNWW6Hl+Q1tjqS3hMUoysGAA88Kd18Ic1sJYkIVTsQw6My2ithff9P8BoOAK5bA7k0pmQCseHjrGdsSjyWnFMv+wqWLI/mWPDBY/uCASyE0ZlY8fBBnzt1ytxyfwxMAFUHI3OagLWerUn3/wUAgwIBeOg7XgH2vFdcMy/EbaJ53LNGhGlHIM0IiKiAaIGgq2y4gycM2Mkdh7q7FO2dE4JbvpMecQDfzAoircBdFVdR8TXxXlpvR5ji5GhWrOjJiJAC+4XHFswk6af3A+jvR6ui/8DcumsXq8lJhOkBZpyJ3NsOC2QkfP6o7ONb687Gtc5TIgQzrwK7omL4Vv5FPwrn4Z2YD2cF9wJMS27T+MjIgrHII2IiGiABKct/sd1ZwIAhLD6+l0DNCA8SIvv/L6wwCor3Y4f3zG/12NiFQF58s09Uds6fBry3AKm2Y6iULQDmA9b+ULIo2f0uq4sqHREZ+GN/cdb0O5RMbM8v8djUpVJa2y12hwE2x/Ec7qGFh+8ihbR9Fs3TIhZI+C6/DtQ96yAums5BMnWp7EREXXFII2IiGiAaIEgzWHvLAgSFGvKnBTYFm8DaL+iI9NtR2uHgpZ2Ja5jgmMJqm3yxNzPdmIHrs5aD2eGB+2GG6Z2LQTZEXeABgATSrKwaHoRKo404SfPbgLQWQK/O8E1abGKlSRi+aYq3HDBBPgUK5M2ZWwutoUFX7F8+/G1McZj/V0Iggj7lCWwTTofgijC1DV43/st7NMuhDx6ep/GSkTUt1W4REREFJc2j4J/rzgAoHOKYbDUfncSne7oUzSMDExxHJHb+1RHAJC7pJQe+NM6AFZREwCwQcMNaetwZ8ZH8IrpeKx1Kf7m+iIE2RHX+aOuJ4nQ9M7v57U1h2L2jwsKBkV9ne4IADUNHaFM2twpI3DumcVR++yvaoFhmhHrByPG02W7EFifZ3Y0wmg9Ce/bv4LnzZ9DPbwZev0RmIq3z+MmouGHmTQiIqIBUF3fuV4sGHCMH5mFBz8/G+NHZsU8JphcizuTpurISXfgh1+Zh8xeAsCgUQWxM2FFuWn4zs2zkN5+DPZVh/CBdyqax1+MvbX1mCD03g6gO7IoRgRAr6w6hHPPHIns9NhBX2hNWgqKc3j9WmhNmtMuIT/LFbXPT57bhG/fNAu/+MeW2OPp5u9CzCyE+7ofQd31IZRtb8L33m8BAO7PPQLB7oJpGqEecEREvWGQRkRENAC8ih5ze3lJ9wUngsFcnIk06LoJWRIxKt8d97hyMhx44jsX4CfPbsLB6tbQdqdNwsQxOQBy8L1VN2PO2WVAs5UV2l/VEvf5u3I5JXT4NDjtUiir1bVISTjdMCGJQkoqKHr9OnyKDkEA7LKIspGZMfera+7Mft34mXL8c/m+iPF0R5BssM+4GLYp50OvPwKoPgiZIwAAvuV/hGBPg/3sz0J0xb4uEVEQP9IhIiIaAL4YVQV7k+h0R80wuy2p3+N1BAHTx+VFbMtSTkDZswKmaeJ7d12AyxeOxcVzxyR87q66FvAAAEXtvsWAEQjSkhW+5u6xV3bgzU+OwC5LEAQBU8fm4pG7F2Bcl2Dtr29XALAKnczqUtiku2mQ4QTZAbnoDKuoiiDANAwIaVlQ965CxwsPQtn9IUwzvrYKRDQ8MUgjIiIaAMGg5OdfXRD3McEpfvEGabpuJB3QhBfmsENFScXfoWx+FVA7e6+FN75O1iXzS6O2PfrCVijdZNM0w0gq8Az6wzfPxR/vOw/FeWmhtXATRnUGZYXZLpSXxJ5uetHZo+F2Rk460vU405phBFGEc+EtSLvuh5ByR8O/+hn4PvoLAzUi6haDNCIion5y5EQb7v7VCnyy8wSeeXcvAHS79iqW0HTHOJ/lramByf3XPmN8Z8boOvcGyN4GOC+4C4K9c91WrHL9iRqV78aCqUUR25rbFRyv74i5v6IasMnJr4ETRQF2m4Tv3z43FGSeN3NUxD7Xnjceowqip4iOHpEOpyMySAvvbZcoKWckXJd/B/Y510CvqYDZ0ZT0uYjo9MYgjYiIqJ+8t/EYFNXAX97YHdqWSKAjJZpJS3K6IwCMLkzHnVdOwVn2g5jnOID28RdCHjkpYp9UZNKA6LL/ALCjm3L4HT41KpuVDFkSkRYIuLLS7VHvXX/+hKhjstx2iIKAWeX5oSIu76yPr/F1dwRBgH3WlXBf9yOI6Xm9H0BEwxKDNCIion5SVpwR8fXSs0oSOj60Jq2X6o7tXhWfVtSitUPpU6l6SfHg+rT1OKgWIHvhtVHv21KQSQMQqoRy02fK8e2bZgEAmtr9MXf1+DS4XalpFu0MBIfpMc43Y3wefv/NcyK2Ba9777Uz8OR3luCqxWXo8Gnwd1MEJl6CIEBwuGHqGpSdH8DUE1+vSESnN1Z3JCIiGiC5mc6E9g+V4O8hk9baoeCbv1sd+vrg8dZu9+2NJqfhmY5zUDSuHGe6oqdlynLfKywCwFWLy+BTdJxzZjGcdhnFeWno8Kox921u96M4L/5qlT35+jXT8cGmYxiRE7uHXJqzM3j7wrKJUWX/czOte9LqUVBgjy7fnyi9pgL+tc/B9LfDcdbVfT4fEZ0+GKQRERH1k67l2u22xDJR8VR3/OlzmyK+7vDFDnZ6Y/raMfuMAmzdPxuXLome+gcgtN6ttCgj5vvxykp34M4rp4a+drts6PDFziY1tPiiKk8mq6QwHbddMrnHfX529wLIkoicjOggNcNlTZNs96ooyO57kCaXTIM8YT6Uza9DHjsbUl7fq2cS0emB0x2JiIj6SdcgLdHpgp3THaPfMwOB28kmb8R2RUu8YqDedBztf78P4rFP8dWrp/WY8fvel87Gt2+cmfA1euJ2yBHBpWGaeO69vfhwcxUUzYDLMXCfKRdku2IGaACQHmgQ/sQbu3H7Ix+i4kjfC384F34egtMN34onYRqc9khEFgZpRERE/UQP9NQKTptLtPBGdyX49xxuxJd/9hEOn4ie2qhqia2XMg0NvhVPQJDtkIon9br/mBEZEdMCU8HtsqHD2xmgNLf58eHm43juvUoA6FOftFTKCARpNQ0eAMCq7TV9PqfgTIdj8RdhNByBuvP9Pp+PiE4PnO5IRETUT4KZNEkSYGhmwiXsg5m0plYfapu9KMx2Ye3OGjzxxh4AQOXR5qhjYlUp7Imy/V0YdYfgXPp1iGmx+4X1N7fThoZWH+qavahv9kZNfRwyQVqXgiNtXiUl57WVnQVj5mWQiiam5HxEdOpjkEZERNQPVM0ITYcLhhiJZtKCwcmzgYzSUw8swY6DjaH3BVFAptuO1g4rWMjPcuL8WaOiT9QNw9sKZcvrkEtnwTbu7ITGlkplI601bp/urcW/PzoQ9X5fKlamksshw24ToahWhrTN0/36v8ZWH9bvOYlF04uRmWbvdr8gx9zrUzZOIjr1cbojERFRP3jho/2orGoBYJVcB5Kf7tjdNlEQMDasiEeWu/dgIJx+ohIwDdjnDW6AMHfSCADApxV1Md8fKkGaIAgRzcjbPbEzaR6fhm89thb//ugA1u06Gff5DV8bfCufgt5U3eexEtGpjUEaERFRPwgvKT+5NAcAkJlgECXHCOrCp/4JgtVyrLQoAzcvLcfXrpme0PltZXOQfsujkLJHJnRcqgWDsEM1sdsHyEMkSAOAb904EyUF6ZhWlouGVj+a2qL7u73w0b7Q67ZuArnuqPvXQ9n8ap/HSUSnNgZpRERE/SC8suNXr56Kn929ACUF6QmdI1Y1SDFskyAI0HQDNknE0jmju61K2JWpeKAd2Wqdw5GaHmT9aahk0gAgP8uFH3x5bqgC5u9e2h61j1/trLDZXWuBWERnBuzTlkI7sAF60/G+D5aITlkM0oiIiPqBEQjSXA4JNllKqq9WrOmR4eX4BQHQdAOyFH8QY5omfCufhve938Foiz29cDDcccUUAMCNMXq0DaUgLSg4pIZWX4z3Osfr9SdWVt82Yxlgc0DZxGwa0XDGwiFERET9QDdMFGQ78f3b5yZ9jlhBWniGTgxk0hz2+EviqxUfQzu4Efa5N0DMKEh6bKk2f8oI5GU6UV6ShX9+uD/iPVkcep8plxRaWdFY2cvw4WoJ9q0TnRmwT10KZeub0BuPQ8qNvxAMEZ0+ht5vPSIiotOAYZpwO21w2pP/PLTrmrSPthyHEtYHzTBNqJoZd5NsvbEK/rXPQyqZBvuZy5IeV38QBAFnjM4OFVkJNxQzaRcEqmjOGJ8X9V74ukFVT7y5uH3GMtgmnwfB3n1TcSI6vTFIIyKiU5Jf1bGxohbt3u7LoA8m3TD73N/LJksRXz/77l5s2ts5RXH5p1WB6Y69/3du6ip8yx+DYE+D8/w7IAhD9xHgifsvwL3XdhZBGSp90sIJggCbLELTzaj3wpKd0JII0gRnOpzn3AYxPToAJKLhYej+hiYiIurBpxW1ePyVnXhj7eHBHkpMhmH2OQPUW3DiVbS4gzRBssF+5mVwLrlr0JpWx0sUBcyckB/x9VAkS0LMIExRO7OdaoLTHcPpdYegbHsr6eOJ6NTFII2IiE5JitZ7Q+HBpBtmzD5niRpV0H31RVUzoOoGbHLP1zFN617ZzlgEedSUPo9pIIRPe0y0dcFAkSUReoxMWmFOZ5GYZDJpoWMPb4Z//b+hN1YlfQ4iOjUxSCMiolOSaZoRfw41qcikAcAPeig80uZR0eHVIPWQSTM1BZ6XfwC1ck2fxzJYJowampk/WRJjBmEuh7UOcerYHKha8j+f9ukXW5Ue2TeNaNhhkEZERKekYGwWXu1wKEnFmjQAMQtphAv2SYvFNAz4VjwBo/4whCE+xfFUJIkCVm2vwT+X78Odv/gotD3YfsFuk5IqHBIkONNhn3YhtIMbmU0jGmYYpBER0SltyGbSzNRk0uIRq8KgaZrwr30e2sENcMz7HOSSaQMyllT671vPwo/vmDfYw+hWMMP33sZj0HQz9LMYDNIcNinhEvxdWdk0J7NpRMMMgzQiIjolGcEH4iEYo2m6gZqGjpRVJXzqgSXIy4wsx37rxRNDr6eMzY06RtnyGtTdy2GbsQz2My9JyTgG2vhRWSjO635N3mC7aO7oiK+DWV09LJPWlzVpQCCbNutKiDmjhuwHEkSUegzSiIgSZBimVfrdowz2UIa3wPOqMcSitHavijt/sQKKaqR0bF1nPQb7dHVL1yCXL4Jj3g0pGwNFykmPbGStqFZAZpgmBAGwdbNmLVGOmZfCcdbVvU59JaLTR/IdNomIhqm9x5rx+Cs7Ud3oxVULSwd7OMNWMPwxhlh2ofJYc+i1P6wUe1/lZjhQ3+LrdT9TUyDIdjjOvhamaQzpfminuvQ0W8TXiqZj6846bNhdCwCwyWKfSkXkaVMAACAASURBVPCHM00D2uEtELOLIOX0EqAT0SmPQRoRUYIaW60H5abW3h+Yqf+E1v8MsSDNr3QGZloKM2lf++x07DzYAIdNwugRGVHvm6YJbd9a+Df8G65L7oOUN5oBWj+TxMj761d0PPHGHgCAAECWhT4VDomgeOFb8QTkkqlwXXhPas5JREMWgzQiogQoqo631h0BAGSmD83eTcNFMDYzh9h0R0ULC9JSlEUBgMw0OxZOK47aLsCEVrMXyqcvQ6+pgFQ8CWJGdCER6n/LN3VWYJw8NgeyJMI0Ad0wogK6RAkON+zTL4Ky+VVoJyohF53R1+ES0RDGj9iIiBKw+0gTaho8ADBglfuoZ0MsRgs12Qb61sg4HgJMPJj1Kryv/xRG03E4Ft4C16XfgmBP69frUmzH6ztCryeX5oRaI6RqyqP9zEshpOfBv+pvMA0tJeckoqGJQRoRUQLaPWrodaoevCg5wemOe440dbvPSx8fwO9e2o4On9rtPqkW/nOh6qmLIE1/B9QDG+D7+Cl43/2NtQ0C1vknwHnBnXDf9AvYp10IQeIkmcFS1+wNvTZNwGGXAEROge0LweaAc9HnYTQdh7L93ZSck4iGJgZpREQJqGm0PilPRf8j6pvw8Ke7dWlvfnIEW/bV497/XQXdMHC8rh3rdp3o13EpYcVC9BRk0rTju+F58+dof+Ye+JY/BvXQpwCsAiEA8KFvGmzlCyHYnD2dhgZAa0dnxVdF05HmtALmyqqWlF1DLp0F2+TzIWYWpuycRDT08OM2IqIE7As8bGWk2ZhJG2ThPaN8/s4H4qCu5e837qnFU2/tgaabmDt5BKrq2uF0yCjMdqV0XOHTHceNzOzz+cyORhgtJ2GfeTnkMWdCLCiDIEp9Pi+lXvjfvSyKcNmtn8nHX9mJifcuRqY7NetYnefclpLzENHQxSCNiCgBum5ibFEG/KqOyqNNUDUDNpmTEgZDePLM41OjgjRflylmf359d+h1S4eC7z29EYDVKDqVVNWA2ynjWzfOQlFecmvDTNOE0VQNFEyCPGEB5PKFrNR4ipg0JhuLphdj7uRCNLb5Q9s9fi1lQRoAmIYGdcd7EHNLII+ekbLzEtHQwN/4RERxOljditomD0bkpmFknhvV9R3YebBh0MazubIOT725JyKjNJyEf9cef2QRBdM0sanS6lVVUuCOOra2ydNv41I0HTZZRGlRBhy25DJeytY34Xnpf+A7XglBlBignULavSoWTS+GTZYwIicNX7t6GgDgJ89uwp9f25W6C5mAUrES/k/+CdNgVp/odMPf+kREcfrRM5+iw6chI82Gm5aWAwDavCr2H2+JWIc0UP72TgVW76hBbVixgmElLDjt8HUGaUdOtOGOn6/A029VAABmlhdEHfqzv2/pt2GpmgG7nFxwZhoa/OtfgLLxRcjj58ExckKP+z9wy2w8fNvZSV2LUiMnw4FRBW5csXAsAKC2KfLfY7B4SLtXxbrdJ1N2XUGS4Tj7WhjN1dD2rUnZeYloaOB0RyKiOISXUl84rSg0te6tT46gttmLeVNG4K4rpw7omBTVGlOHVwNyBvTSQ0J4ArHNYxVs+Olzm0LrBoOKclO75qw3flWHzZb4Z6CmpsD71i+hn6iEbdL5cCz+fK8ZtDNGZyc7TEqRX3xtIWBaxWuO1bbjM2eVRLxv7zId2jBNiEJq2nfIZXMg5o+Ff9MrkCfMhyDZUnJeIhp8zKQREYXZV9WMFz7cDzWsIbHHp4UyNeeeORJjizJD09iCWazdhxsHfKx6oDCGZwDLyw8l4dMdn3prD1TNiArQAGDmhIK41w3+/YNKPPinT/p0T5PNpGmHN0M/UQnn+XfAee5tEER+jnoqEAUBoihAlkR847oZmFqWG/G+vcuU164FbfpCEAQ45l4Hs70B6u6PUnZeIhp8/B+AiCigw6fip89tBgBMH5+HyaVWeuqe/10Jl8P6dVlalAHAejiaMDob+481A0hdH6R4NbT4Qtm9ruuxhovgWrwZ4/Ow/UAD6luip33+9j/OQZpTxq++vgjtXhVb99XDr+p4dfWhmOf84NMqAMDRk+2YVJpcelLRjKjsSTxsE+ZDzC6ClD82qevS0OS0xwjSUlicUxo1FbYZyyAVlKXupEQ06JhJIyKC9cD/v//eFvr6iTd242RjZ3EJbyAQCn/4/sIlk0OvRTE105fi1dDqC71u9w7TTFogITGrPB8A8Mt/bo3aJ91lC/1ZlJuGZfPG4KrFZXjqgSVYNncMAGDFluNRxzW2+aK2xUvVEpvuaCoe6I1WcMgA7fSTk+GI+Lq7nn7JEgQBzvk3QioqT+l5iWhwMUgjIoJVKfHA8dbQ101tfqzdeSKqcmL41KXwku/SAAdpSth0zOfeq0RTWKnvoNXba7Cxorbfx1LX7MVLHx/AxoralD+A9sQEIABIc1qBWKx70JPgPXx97WHrfOF91/qQGVUSnO7oW/M8PK/8EIavLelr0tDltPfcvy9VjNZa+Le8DtNkpUei0wGDNCIiAM3tStQ2zTBQVdcRsa04t7PvVTA4AKxPs/tTdX3kOFQ18kHsvj+swcsrD0Zse+qtPXj8lZ39Oi7AatT75idH8PgrO7Hz4ECuzTOtLEJgOpksibAnkMH67LnjAAC5mVamIzww68v0VUXV4x6HeuhTaPvWwD5jGURnRtLXpKEt+LMGWGtJaxo6sG73iZReQz+xD8rGl6Cf2JfS8xLR4GCQRkQE4Pn3KwEAP71zfmibohhRmajw5sQj8zv7b6W6obVpmvjL67ux81AD1u06gYeeWI8dYT3ZFM0K0iaMygpt21fVjNYOBSu3VaOxNfnpegmPNez1O+uPDNx1TUAQOqegarqB3Axn6P2i3J4bSac5bZg5IR9q4F62hU0b9fY5k9b7z4PhaYZ/5V8hFpTBPvuKpK9HQ9/lC8fi1ovOAAAYJvDff1mPP7+2u5ejEiOXzQFsTmiVq1N6XiIaHAzSiIjC5GU58auvLwIALN9chYPVndUCHTYJstT5a1MKe921gltftXtVfLLrBH79r23YfbgJAFAfqCRZVdeOPwWa4l61uLNYgGECH2w6hr++XRGVVetPhdmdJe4rjjYP2HWDsxPD731ZcWbo9cNf6r1/mCyLONHowdGTbegIC9J8SvLFWFTVgK2X6Y6macL38VMwNT+cF9zBSo7DQHDdanVde2hb8AOCVBBsDshlZ0M9uBGmltjUXyIaehikERGFkUQhYqH//rCS7rPOyO/2uGSq+fXkw82dxSyC5f5V3YpK/vXh/tB7owrcuGTeGIzMd8Ov6GjzWIHGyQFscB2cbrj0rJKEphv2lRmY7hiexZxVno8LZo/CN66dEbpvPbFJIhTVwPee3hjxd92X6Y66YUKWepn+ahqQckvgmPc5SNkjk74WnTqCvdF+EVbgxpviyqy28gWA6oN2dFvvOxPRkMYgjYgoTHBt2fRxeQA6pxUCwMQYjYNvXmpVVEt1dcfwwiDBSo7/XL4PLR1KRHbP7ZRx/QUTUFLghk/R4An0cwsGawNB1QwUZrvgcshQVSOq2Ep/CU53DBZtGZHjwpxJhbj1oomYWd59QB3Or3be5xNNVjVPAX0rHGKYZq9rFAVRgmPeDbBPW5r0dejUEut3RKqDNKl4EoSMApjtDb3vTERDGoM0IqIY/vOGM6OyQuFTHYOWzhmNWeX5OHKiLSUPXKZpYuv+ery97mho29b99aHXb6w5DK+/M4AITqvLcjvQ1O4PNWEObx/Q3/yqVXLebhNhAqH+bf3OtAKq4MNvVrqj5/1jaOnonBbWHKgOmZfl7FuQZpihrElXpq7C++5voLG4w7ATHqTlZ1lrJ9UU/1sRRBHuz/0M9hmXpPS8RDTwGKQR0bD3WjeNjR+4ZXbE190liDLddgDAx1ur+zyWZ97di9++uB2AlRnqavnmqpjHFea4oKgGamIEZ/2Z2Tp6sg0VR5vhdsihsvN+dWCCtOB0xxE5afj8RWfgq1dPS/gcU8fmhl43tfkhAMjNdMLfhzVphmlCjPG/q2ka8K38K7QjW2D6WqN3oNNaeJuOjDSrMqyup/7fphD44TO16Iq1RHTqYJBGRMPeK4Egbd6UERHbxxZ1FqFYMLWo2yl0t1xoVW1r8/b9oagmrNT+deePD712h/VkS3PI+O9bz8LXwoKS4Dq6xtboggHdFfPoa+bPr+j43tMb4fVryEp3hNamfbw1ujl0fzCDjdIALJldgqxAsJyIKxeX4cuXWU3JD59oQ5pTRppDTjqTZpomTBNRmTTTNOFf/axVbv+sa2Abe1ZS56dTV/jPRLrL+lnV+6lnmve938L7wR/65dxENDBYToqIhrXwLFNOjOlyZ4zORptHwR1XTOn2HLIkIiPNFjENMVn+sDVw4SXkXQ4ZHYH1Zj++c35UQJKVbkeu2IY59kMolpqRKXph2NNR47Xh8HZgcumSiP33Hm3Cz/6+Bfd9biamluUiGb/455bQ60mlOThrYgGefrsCq7bV4LIFY5M6ZyJMMxSjJU0UBIwPa2OQ7rLBYZeSD9ICf3Zdk6ZsfBHqno9gP/NS2Gdfmexw6RQWPt3R7bIev3Sjf7LOQuYIaDveg+lrh+BM75drEFH/YiaNiIa1NTs6G8oumz8m6v0HbpmNH98xP2p7Vy6HnJI1aYqq44ySLHzt6mkYVdD5cPWlSyeHXsfKGOWkO5AlenFZ2laMkeshwESR2IR5jgPwIfCpfcMxaFW7YBoGNu2tAwAcrW2LOleHT+21NLhhmjh60jq2OC8NF8waFWruXdvsxZ7D/d/UOjjdsa/Cz5DussFpl+BTkwvSjEBmJLxGhGkYMJprYJt8Puxzr+/3xuc0NIVn0tyBfytGP2XSbBPmAaYO9fCmfjk/EfU/ZtKIaFjbf9yaCpjusiEzLfHpckEue+9BmmmaMEwTUpcFS16/BlU3kJlmh6IayC+2qhSGm1yaAwBRpd21Y9uhHduBjLk34oiWj2833oT8/GxU13egLD0DrX4Fk2BVpVR2vAOtcg0ERzpmSaPRZs+B24ic4rlqezWefqsC5SVZePDz3U/JUzUDmm7i2vPGxcyatQ5AdclUZNKAzvVBAOAOBGmtHQqef78Snz13HNq8KkzDxIhemmNbYwoEaYEozTRNCKII59J7gBQFlXRqCm8Vke6yfua0fgrSxLxSCFkjoB1YD/uk8/rlGkTUvxikEdGwFpzWlubs269Dl0PqNUh79t29WLG1Gk9+54KIh/UH/vQJ2jwqnnpgCRRN77Yx9u+/eU7oONMwoGx9A8qnL0PMK4HD8OO/vzgXe482Y2pZLh5+aoPVr0uWoAYeBJ2LvwhtzExoR7ci78A23Jy+B/Vbt0M9+zehKpFPv1UBANgX1jMslmAFR1tUxUsTLkGBqHREH5RqgRL8fRXMAALAuJGZoWIOyzdVIcttx/8FGoNLooAffHkuivPc3Z4rOHtNFATo9UfgX/McnEu/BtGd0/eB0ikt/MOAYJDWH4VDAGu6rW38PChbXofhaYGYltX7QUQ0pMT1VPL73/8eb7/9NgDgvPPOw/33348HH3wQmzZtgstlVR+75557cOGFF/bfSImIUqi1Q8H9f1wbanicl+ns0/lcDhl1vTSQXhGo/rh25wksml4c2h7e00xRjYjG2DdcMCHUMy0YTBjtDfB99BfoNRWQJ8yH89wvQZAdKCsGyoozYZomrlw0FvOnFuGxl3eGpi4Ksh22cWfDNu5svOSvwJ7tu5AheDF94zGMK3Kj6NPf49q8EfioaRQ8cs8PdVrgnHJgrHpjFZTt7+LH2RuQLvqBDUD73hFw3/AIBEGA3nAUgisTQEY8txMAcKLRg4YWX7dr5lI13THcnImF2FRZF/r6k12d02F1w8TGPbW4cnFZt8cbgUxamr8O3rf+Bki2bvel4SU7o3PNa3+vSQMAW/kiCOl5EGyJt6YgosHXa5C2du1arF69Gi+//DIEQcBXvvIVvP/++9i5cyeee+45FBYW9nYKIqIh52BNKxTVgBIoF3/XlVP7dD5rTVrP65jGj8zEgepWbNtfHxGkBemGAUWNzKQtmxe5Ts40dHhefwSmrw3O874M+YzFUYGKIAi4+pxxAABRBBrbois+frilGkAOapCDyo8PIkvw4OZ0BefaNuDcbMBvymh/6UM4590AuWQaDG8r9BOVENOyAckG/UQVLnVtgcMYDQDQ9q+DdmA9dqslOK7n4qzyPEwY4QyNzbfyaRh1h3FyykJg9g1xfbL/3T+vAwA89cCSmO/3R2cBl0OGQ9CRJXSg1XShpiGypYHQS9Ny0zRRILZi2oH/A+wy0i6/n1k0AoCI6dT9vSYNAMSsEbBnjeh9RyIaknoN0goKCvDAAw/Abrd+uYwfPx7V1dWorq7Gd7/7XZw8eRIXXngh7rnnHoixGsMQEQ1B6WFT3MaPygz1OktWPIVD3IEpTrVNsTNuL688BBOAwxb9u9TUNUCUIIgSnOfcBjEjH2IcD2BHT7YDCPTu6iHr1GKm4fG2C1EotmCisxb5aMZCEXDJ1qfwes1e+MJKetsALHUKOKIsAlAO+5mXwD5jGd74y1a0+BSs2AE8cekFof2di78I7eAGeHZ9ABzaAef5X4E8eka342nzdLYzMM3YGTMTfZ/uaHQ0QTuwAdenbcK/PfOR5pRxRtXLWJCzEx7DhmN6Hqq1XDQYbqzyTwZME1rNXgg2J8S8MVHj0puO4z8z34IAG1yXfRtiVlHfBkinldsumYS/vl0RCtj6qwR/kOFrg3ZgPeTx8yA6489iE9Hg6zVIKy8vD70+fPgw3n77bTz//PPYsGEDHn74YWRkZOCuu+7Ciy++iBtuuCHuC+flsSRsIgoK+Ms1UbxniRtO9+xEWD+x/Oy0pL/34HF5OWnwKRry89O7nYIXXH7S6lFDxzWHZbneWncEAJCbEzke3dOKE//6GdyTFyB7/lVAQe/VJoMcdgl+Rce2Q024aF4pAKuCZJpTxjkzR6HNo2Dt9prQ/rVGFkrGlGNVRS1GnzkTk6ZbxxjZi6COKYXuaYXf60WjkY77/7Yf942dEhirNd7ffSsHP3hqPfYfa0ZmVhpcDhntHgXu/GkQpkyHMvdCnHzlUXjf/jWKb/0hXGNitzZ49dWdodeZWWlwOqL/u3I6bZAkMam/O9M00brpXTS8/zRgaJhsS4cDKkpGZuNQ6UK8UOXGKKkRo+VGnOOsQL2RgVX+yZBkCerqp6E1nYB9RBlcY6dhx6YdcI6bifnX3wpJL8AurQDOuTfhwjMmJTyuWIbTv8tUGar37NqlE3Ht0omorrM+PNl9pBmXnTuh367nr6nF8TXPITM3GxkzLuhx36F6z4Yy3rPE8Z7FL+6V8vv27cNdd92F+++/H+PGjcMf/tD5ieqtt96KV155JaEgraGhvV/T/KeTgoIM1NVFl8mm7vGeJW643bOGhs7CFg5ZTOp7D79npq7DMIGq6mY47bF/tbYHskPN7X7UnGiBJAr4+qMro/ZT/VrovEZ7A7xv/gJGez2EqRdDTXCcD9w8G9//60bs2FeHWeOstV3ffmwNPD4NuqrjK5dOjgjSAKBsRDo2V9SiurYt8r7IhUBmIW5/7EMAdQAkeDr8UfduweRC7D/WjOPVVuXM//z9GpwzoxhfunQyCgrHwHHFQxArV6PNWYL2ujaYiheC3RVxjrfXHgq93ne4IaJnXJDHo8A0zIT/7kzVD9+aZ6FVroY0egaciz6Pn79wCH50oK6uDY1pZVjj93U9CgDQ1OqD4+L7IFXthLp3JVo+fQcuxYkVW6ox/vw2tHhl/Ln9M7gV2Sn59zTc/l2mwqlwz4xAwaJD1S39OlZTKoCQlo2mHZ/AVzyn2/1OhXs21PCeJY73LJIoCj0mreKan7hp0ybcdtttuO+++3DNNddg7969ePfdd0Pvm6YJWWahSCI6dQSrEwLAdeeP7/P5goFZ+Lo0VTPQ2OqL+DqopV1BQ4sPPkWHq0uWKFjMRG88Ds+rP4LhbYHr0m/DVtb9Q1Z3Sosy4LBLWLHlOAzTRE1DBxoCWURbYFrlnVdMwY1LJuCRu+bjc0smYMnsEgBWxq03sSpROuzWNr+qh9bDrdpeg6paK3sgyHbYpyyBIAgw2hvR/o9vwf/py9aUzoBJpZ3ruH7+983dXD3JGvyiCKPuEOyzr4Rr2TchZhbi/902B4/917kArObkAJCf5cTE0dmBg4TQ9yRmFsA+5QK4r3kYGV/+C37Ucg1W+62sWfCzx97WrtHw5rBLmDOxAMdq2/H62sP48+u7sLrLhyWpIAgC5NKZ0Kp2wtSU3g8goiGj1yCtpqYGX//61/HLX/4Sl112GQArKPvJT36ClpYWqKqKf/3rX6zsSESnlPAgLVgOuy/kwEN5+AyB3/3fdnzrsbXYuq8eQGCaYSAga/Uo2HvMyjTddWXklL+8LCdMfwe8r/8UME2kXfFdyMUTkx5baaH1SV2HV8XLqzozVMHy+fOnFuGiuWNQmJOGi+eOgcshwy6LoaIq4cwu1TpyM6Irxzls1vfoU/SItWXrdp+M2lewOSCPORPK5lfheeUH0I7vhmma0MP+fprbYz9cxtsnzVS8UHZ9gI5Xfmhl7SQb0q55GI45n4UgWPfAJkuhQDsjsF6ovCQLd4YVlBEQHbh2nRHS2cyaQRr1LFgZ9eWVB7Fu10k89daeiA91Unad0lmA5odeU5HycxNR/+k1/fXkk0/C7/fjkUceCW278cYbceedd+Kmm26Cpmm46KKLcPnll/frQImIUkkLLBAbW5Sa+fHB5sV6WBDT0GI9cB2sacXM8nwomoEMtx0ev4Zf/nNrqNCI0y7jm9fPwP/+ezts0JDtkiE43HAsvBnSiHKImQV9GtuSs0pQWdWCdbtO4tOK2tD28Oa6XSmagXc2HMWi6UUYVdA5HWPNjs6S9PlZTuRnuaKOTXNYmbQOn4b1u63ruRwSTjR6ovYVHG64LrgTatkc+Nc8C++bP4dUPBG6tjRiv5NNHizfVIVrzhkXyjzqRnRj8CDTMKDXVEA7sA7q/vWA5odYUAbD0wTJ7oIgd18oZtKYbPzg9rkozHHBbpPwnZtnYf2eWuyvaonIhgKRwT7QWYKfQRr1RorxM/KPD/bh65+dntrrjJwM2JzQG471WKyHiIaWXoO0hx56CA899FDM92655ZaUD4iIaCAcPWnNi7/76mkpOZ8UDNLCHtqDD/CtHdaUP1XTUZjtwkkgohJkfpYTrSeO4pq0jZhrPwBHQy6QOwe28oUpGVswM/SP5fsitk8c03tp+P95ckNECfyn3toDwJqu9fOvxh5fXpbVc66+xYsNe6zs2biRWWhq6z5LYBs7G/Lo6VArVsJoq4PSJGDq2Bzk1azFcT0HD/1pDXRISHPIofYCumFCkjofdE1DBzQ/BHsajJYaeN/8OSA7II+bA/uUz0AqHNfr9wtYU8RKCjsD04ljcjBxTA5+8NeNUdX4ugZpwUwjYzTqjT/GdOL+qPYoyHak3/Jo1LpPIhrauJCMiIalzYEpiOnO1PwalAJTBz/afBwLphWhrDgT/kBxgA6fhtfWHILXryMjrXNqpQ0aLs49AufH62Gr2YvFDhHblDFYmJWfkjEFZaZFTue8bEEprjlnXCj7l4zZ5d2PMdjOoN2jQjdMLJs3Bi3tCk7GyKSFEyQb7FM/AwDQdm9AplPBte6N1rkMBzYpZVDrdBhGKURRgq4bkAUT6oENUCtXQz9RCbl0FlxL7oKUMwquZd+ENHIyBDk1zXxlWYyRSYt8qA5OEe3LvaXhwadEB2lFedEFclKBARrRqYeNzYhoWJJEAZNLc5Dm7Pt6NKBzetsHm6rww799isZWH1o9KgCgvsWHVwJrwURRgATr4UyHiLOFnTC9bRBnX4uHm6/FMx3nwl44NiVjCnJ3WXM3qTSn1yDiN99YHHodzPrtPdoU2nbT0jO6PTZYTORgTat1facMp0OK+VDaHVU3oNoy8GDTDfhL2/moVIuwyFGJpfXPo3LVBwAAl9qEz+NV+JY/BqO5GrbyhZDHnR06hzxmZsoCNMBad6jrPU93XL65yhpbNxU+iYKCBWrCOWMU4kkF09DhefvX8G99s1/OT0Spx/9FiGhY0g0zIqvVV1KXoOflVQdDr+ubO5tXj/ftwmVZq/HTliuhQsbbuV/AndeeBQAYd2gbnPbUP6Rlp0cGKsUxytl3FR7Y/fKfW/E/X5yDV1dbgWZZcUaPxVaCAeumvXUArAIlTltiQZqmG5BlEVMnjsHGCid2qmPg6FAw3V6F0cZoTAJQ4j+AAjTAef4dkCcsgNDN+rRUkWURHd7IhuVdg7Rgpm36+Nx+HQud+i6cUwJNNzBlbC7ys5z402u78Praw1g8oxi5mc6UXksQJZiKB9rBjXDMvCyl5yai/sFMGhENS4ZhpHRKWtdzjcxzAwCmleWiw6fBJSi41b0Ki9reQYORDptgPezffGnnQv5vXn8m7r4qNWvketJTwZAgURCQE6jceCiQEQtWLnQk+Gm/TRbhsEvQdCNqumB3vH6rNcHdV3VWV/TDjk+VcZDTrHu7X5qAvzm+ANsZi/o9QAMAWRRjZNIipzv6FB0lBe5uC5oQBU0em4v/+txMLJs3BnMmFUI3TOiGiT+/vrtfriePmQmj/jCMjqbedyaiQcf/RYhoWDIMMyr71RfhBSxssoi6QPYsN8OBK1yb8HDWS5hlP4xdmYvxeNtSeEzrk/JUlP9PVDxBGhBZxGDDnpPwB9ZbxeqN1hNZFkNTu3YdrO9x3+Z2P25/5EO0e1W47DKEGBU4gtmsNrihSO6ExtIXsixC7WW64+bKum5bBhDFI7yoUCrJpbMAANqRrf1yfiJKLQZpRDQs6akO0sKCCVUzsGnbAQBAepodRVIL9qgj8avWy7AncxGMwK/eH35lXsqudD1vFAAAIABJREFUnwi7HF+QFZ41+uDTKrhd1gz58SMzez32nBnFodc2ScSZ4/MAAK0d3QcwumHgz6/tCn3d3O6PeH9kvhtpDhleRcOx2nZUHG3G8fqOuL6XVJAlAbpu4uWVB/HCR/sBRGbSdh5sAAC0e9UBGxOdPiaNsRqnuwPFjFTNwKrt1ThQ3ZKS84s5IyFkFEA7siUl5yOi/sUgjYiGJcMwIaZwSpo13dFEmVyLL6d/hO9nv4hcsQ1pThlPtJ+Pv3Wch+N6LorzOjM/sRpB95erFpd1GWvvppZ1rqsyTRNjCq2ecpctGNvrseEZQpssIj3QBuDg8e4fOIOBV9DVgTGXl2SFxmC3ifho83E8/NQGAFblzIEiS1Ym7fW1h/HO+qMwTDMik/brF7YBAC6YNWrAxkSnj3uvnQFZEkLZ471Hm/D0WxX49b+2hfapru/A7Y98iGO17QmfXxAE2KdfBHnU5JSNmYj6DwuHENGwpBtmzGayyXK0HMZ/Zr6NsXI9Ogw73vdNh8+0w+WQYUJEaVEGblwyAeWjs1GUm4ZWjxJqyjwQrlpcFir8Ea+vXD4FOw82wuPXoGoGFE2H2ynHFeSFV66zyWIoO/DmmkO4bN6YmMd4uwRc+dlW2fBL5pdi34vbke6yhdbFDQZZilyTpqpG1HRHACjIZrlzSpzLIePsSSOwr8r6oCLYRy18+uOWfVYxnnW7TmB04YSEr2GfdmEKRkpEA4FBGhENS7phpqxwiOlrR876xwFBxr875mKDfzwU2HDvtdNDfbN03Qw1j57ZQ4+x/vS1q6dhz9H4iwbIkogpZbn4tKIWimaEinnEdWzYujdZstakzRifh/09ZNK8YdUfrw7L/E0ak42rzynDzAn5ePLNPRHHLD2rJN5vp89kSYhYk7ZyezX+8cG+qP3C1ycSJcLlkEJBWdeiNABCBWn60vTaVH0wWk5Ayh+b9DmIqP9xuiMRnTIMwwx9yhyvA9UtMbMdhmGm7GFacKZDOu9OvOS+Ca0li6DAmuo3bmQWRuW7YZdFzJ1cmJJr9cWcSYW49aKJCR0TjGNVzYDHpyItzubfti6ZNMCqeBnrwTMo+HD6yN0LcGVYkOa0y7hyURnGjMiIWkd4w5LEswnJkiURfqXzZylWgBbcjygZLocMn6LD7DKVdtt+q+BOReBDlr5klH2r/gbv27+GacZXaZWIBgf/JyGiU8a7G47ip89tjmiq3JPq+g78+JlNeHHFgaj3UlE4RG06Ae2oVSnNPeEs/NcXFuPyhWND7ztsIkoK0/HHb50fsf1UElwf09Lhx7YDDXDH2fx72rjO9WzBIE2WRaia9QAaS0ugqEhGDxUvD59oC73+zs2zBjQgkiUBRjdjj9gvhQVpaHhx2iXohhWghQdp1YECOcEPqTp8GnYdbkzqGvKYM2F6W2HUHux9ZyIaNAzSiOiUsW73SQBAbZMXhmH2+sDc5rEe+o+EPdgHGYYZarqcDNMwUPvqb+Bb8SRMpbNZdXjgl2ip+qEoGFxmBAp/FOX13ggbAEoK0kOvg4GUTRJgmt1P1apt8sLtlHucUnnd+eMBWEVNxo/KimssqRJvQMjpjpQsp9362fcqekTW2W6ToKg6vH5rSvAnu07gV/9MrpS+PHo6IIgsxU80xHFNGhGdMoIVzZ5+uwL/+nA/CnNc+H+3nd3t/sFYoGssZhgmTKBPmTR193L4j1fCecGdEOydhSLCz9mXIHCoGJXvRk6GI/S9zJ2U+LTNYCbNFij9r2pGzIDnwPEWjBvZc+B16fxSXDq/NOExpEJ3QdqCqSPw5cun4NEXtmHXoUZOd6SkOe3WvxGfX4vIpGWnO2JWMjXMxD9sEhxuSMUToR3ZCsfc6/o2YCLqN/yfhIhOGeGPIh6/hsMn2rqdOgcg9F7XhsiqZj38yHE2de7KaKuHf8OLcI2fBXnCgoj3pNPwAd0mi/Ap1gNiMvcs2JdNDmSYwh8+K481o6VDgWGYONnkwaiCgWtOnajugq/5U4sgCkLo51NKYWsHGl6CWeQ31h6J+HdimiZUTY/aX1WTW1cml86E0VQFo7UuuYESUb9jJu3/s3efgXGU197A/1O3qHdZluUi915wAwzGxqYYU0MgCUlIhYT05AUCKTe5CTEtIaEkhJJcAoQAoRowNphm416x3GVZlmU1q2vrtPfD7sxWaYt2V1rp/L4g7c7OjNZCO2fOec4hhKQNhmGQaeHRbfcNC3ZJilEiFKy3JhV6a2v9rnUsNE2D85N/AgAKL/s2OtyBAWAiB2QPFgLHwu5t6sHHEYBkWT1rzPSMmh4ky4qKNc/tRnlRBr60YiJkRUNx3uBtX1+Uaw77eLa3FFT/beOp3JHEyeL9m7Tps4aAZkOqpsEthwZkZ1ptGDsi8nD5YPz4xeDKpoLJGphOs4SQyOh2HyEkLWiaZw1a8KDgHofUyysAtzcYC46bnN7HTXGuGePHzIVp8Rcg5ISW/g3FII3nWegJy3gyaXoGSv+vniH4YHc9AOB0iw33Pr8HADC2NPYLzlSZM6Eo7OMVJZ71d7L3InoorEUkA2Nsme/3f/uhZuNrRdVw5FRoZ9tXPo6v+QdryQZXMCqkyoAQMnhQkEYISQt6kBC8/sLmCF2nodMzZv4XIt12Nx575TMAsQdpmuwGwzAQpy6DOGVp2G3088vJFGPa92Dm304/lizRPd9ehB9/fpZvP94Ar8vmCaxPNHSFvGZ0aVa8pzkgJpbnGL9f+gw1Ic4yWkLMIo+vXBo6JkNVNTy34WjI4x09rriPpbSdhvOjp6C57XHvgxCSPPRJQggZVBpabXhnW23I43onR4ZlArJp63ec6nVfenlQ1ck21DZ24+UPq7H7aAtOeRuQxFLuqHScge0/d0A+tb/P7fR1cCV50XVBTAf+QYcQw5q70nwrZowrCHntPc/uAgDYgrKg6ZiF9B+Irg8uj+U9IiTYZO/Qe3+9dUStb7H1uS63L5rbAenIJ5DrPovr9YSQ5KJPEkLIoPLAC3vx0gfVxmBjnX6RwrEMvrhiAu780lwAwJaqJuw60hyyHwBwuT2ZNE0DfvPPHXh7ay3+b90R4/lMa3Qzv9SeVjjefhBQFbC5pX1uW5hrwc2XTcZt10yPat/pwD9I609jlOAMU3CparzlpwPJP0hbPL0EAFCQE37tGiHRCLf2scYv6xycpY8zRgNXXAnGnAX55J74dkAISSoK0gghg4rTG1hJSuAiedUbpLEMA45lMXFULm5YNh4A8OIHx8Puyx2mG5o/axSDmdXuFtjf/AM0lx2WS38CNjtyC/oLZpUZc8WGgtJ8X1bQ2scMs0iCuyPagwJxUxyNXAaaf5B26YIKPP6zpcjsYxg3IZFwLIv7bl2Mn944G7deNQ0A8NHeM8bzk0blBmwfzYD1cBiWBT96NuS6fdCU3tf2EkIGBgVphJBBRS95c7sDAyy9pMf/oviSBRVYPK00bBdHWVEDukAGG1+eg/wsU5/nojq6YH9zDTSXHdYrbgdXNCbaH2NIWTi1xPi6P+utgl9rD5r75F8amS44v/WODMPQejSSEIW5Fkwbk4/pY/NDnls01ZPN12+e9FYKGQ1+zFzA7YCjtirufRBCkoM+TQghg4oehLmC2k0rRiYtcPvSfAvau11GkxDdn1/ah/d3nQ57DI5lcNdN8yIOHWbMWRAqF8J6xR3gisbG8mMMKXoZYn9by/sHMLKihpS03rRyYr/2PxDYNFxHR9JH8O/XnAmFmD2hEE/efhGWzi4D4KsyiAc3chrY3DKoTlu/zpMQkngUpBFCBhUjkxYUdOnXIcHNJUq8d5PrmnoCHq862d7rMSLdeVY6zkDtbALDMDAt/Dy4wtFRnftQpQdp/V0zVl6UaQR6G3bWhfw7RAqaCRluggejGzerWAaM929hfzJpDC8i4/P3IHPqefGfJCEkKegTkRAyqOjVY/5B2rPrj+Dnj2/xPB8UpI3zDnJ9/I3ey3W+sHxC1MdX2urheHMNHBv/FnfXtKFGXys2ZXRo17lYsCyDl/5wBViGwYETbYk4tQG3fF75QJ8CGcJ4jgm4eeE/gkS/YdWfTJpO01RoUvzt/AkhiUdBGiFkQDjdMh78z140tnlm9Ly+qQZrntttdGT0vzu8cXe90VAkeE5aYa4FE8tz0NHjCruAPidTxIr5ozB2ROT5W0prHRxr1wAMC/NF36JBr16ZFgG//Oo5+OYVU/u9L55jMXZEFurPBpZXjShIv5EF588cgaljQtcMEZIoDMPAavY167l8sS+rz/pl0g7VtuNoXeiw62hoioxTj3wH7t2v9+9kCSEJRUEaISRqkqyi02946raDTfj0QENc+6qqaUdVTRte3OjpzPj6phocreuAzdtMorcSnnCztBZOLYGianjwhb1o7w68Gyx5Z1fd8cW5eOC75/Z6PsrZk7CvXQNwAqyr7wSXWxbXzzVUjR2RDTFBLfLzskzosrkBANPHeYKcO7wjFdLFrVdNw5fTcA0dST+yd33uqsWjMX5kjvG43rRGUVXc/+89WPPc7rj2z3A8xMKRkGp2UvUAIYMIBWmEkKg9/kYVfvzIZmiaBllR8fgbVXhy7SE0tMa+6FxfauE//8yfEqZjIxCaSQM82TQAOFTbjoMnPWV0eht0/c6zKHDI66Obo2vnq2AEM6yr7wSb0/csNNI/eVm+OVDXLBmHp+9chuw0G1kwqSIPAp9+IwNI+lk5fxQA37B0ndFkKejxeGRMORdaVzPU1tp+74sQkhgUpBFCorb7aAsAQFa0gDVFdz+xLeZ96WWNn51oxUMv7QvJnCmq78LDv6tguG56hX7Dg/W2+wLP4vyZI3D5Il95ULjyRU32ZHQsy26BdfXPo5qDRvrHP1juz9y1gdTfTpeEROvShRVYNnckVp0b2MBI/1sY3CU1HhmTFgIMB7l6e7/3RQhJDArSCCExc0kKqs909rnNE28exFtbTvb6/NlOp/H1/urWkOf1oM3ulAPmoIlC6J+twCDNE3RJsgqhj26BmuyG8+N/wPH2A9BUGYxoBZtV2Ov2JHHys31BmsWcpkEaSx+fJDVEgcNNKyeFZJv16gNnAoI0zpIFrnwapBPbqeSRkEEiPT8dCSEDyi0pRjAUjqKq2FLVCABYtXhM2G0aWu19HkMvdwy+S1ycF9pgQuA5/OHbi/Crp7fD7V2/ISlq2MHCqxePRnbXMdhf/R+o7Wcgzl4FgLIiqTR1TD4WTytFWaEVWd6y1HTDUSaNDDC99FtvqtRf4pzVgOwCoIH+JhIy8ChII4RExT9YcklKyDqIr6/ZiEd/fAEsJh6y7LsTe/x0J8aX5wRse7bTgS1VjRB4FsV5FtS3hK5pk73ljs6geWnFueaQbQHPvLQMMw9J9mwvy6FBmtrVjJVn/wm1tQ5aZgEsl/0E/KiZkX50kmCZFgHfWt3/TpEDKVwDG0JSSf8dtCcgkwYAfGn0o0oIIclH9RqEkKj86infWoW7n9iGbQebkJsZWH7z1FuHYHNKONvlK2Vs7gjNmJ3xtl///EXjceV5Y43HJ43KxYO3eYaqvrWlFl12N852OAAAs8cXYuqYvps1CDwLt6xCVTUoqmbMF9LLd5iMfDDmbJgv/AYybriXAjQSNxrPQAZahjcLvXH3aeOx/pYqqh0NcO1+nUoeCRkEKJNGCIlKq1/gpSvMteDXX1uAHz+8CYCnscj+6lbIii/LJofp0tjZ4ymVnFVZgMZ2XxA3qjjTuDvc3O7Aj/6yyXjukgWjMKmi72HKIs9BklU43Z47yxaRg3xqH1y7X4f18p+BEa2wrvp/0f7IhBAyaOmzBU819RiP9bdQUWmpgXvnq+BHTgNXMr5/J0gI6RcK0gghcbOaeORkBGbT/AM0AFCU0PbQHd4ZWdkZInIyRVx34Ti0d7tw/UXj4ZbDr68o8rbZ7wvDAI1tdticMjgoGN+4Do69n4ItGAXNZQMjpt/AZDK4jB+Zg+P1fTfNISQVLGE6o2qa5vlDGCd+9ByA4yFVb6cgjZABRkEaIWnM4ZJhFrmUlF6VFWZgRIEVIs8ZTUHKCjMAAD+7cTaqz3Th1Y9PhLxODjOUuqvHDYuJN4Yj+zcXEXgWMysLAjo+8hzb54wz3Wnv2rYH/74OP8n+GEWN7RCmr4BpwfVg+PSaw0UGp5/dODthjRoI6Q+eY8GxTMD4ElUF+mhqGxEjWsCXz4BcswPa4i9QWS8hA4jWpBGSpg7VtuO2P32M1z6pScnxFFUDxzKYVJELwHOz9tKFFQA83frmTgjfvn799rqQxzptrpD1bP6WzCwL+H7F/PKoLhZmVhYAAK607kIua4e05Lswn/slCtBIwogCh+wM+n0ig4NZDFyjm4i1ZPyYudBs7TTYmpABRkEaIWlKb75xoqErJcdTFBUcyxjljBfOHhkwt6esMANXBA1bBULXsu043IzGNkdImaS/4AsNk9B7sxDj/Fpq8K2LSlGSZ8F/7QvQceEdyJ+yIOLrCCEkXeUGVRioCQjSuIpZgGCB2tnU730RQuJH5Y6EpCl9ThmbonIUVdPAsazRCIQPmhPFMAyuvaASLR1ObDsY/sO9odWGv752AACwcGpJr8cKno3WV5CmKTJcW1+AVPU++Inn4Z5vfwP1LTaUF2dG9XMRQki6mj2+EPUtNmRniOiyuZGIpoysJRuZX30YDEuXiIQMJMqkEZKmTjZ2AwBcbhm/fno7/vLy/qQeT1E0sH6ZNL6XhQ83XzoZAFCQHbqGzO03W02WQxuK6EYWeQIsvZQn3AJ5ANBcNjjeeRBS1XsQpi2D+dwvgmEYCtAIIcPCivmj8KUVE7Fk5ggAicmkATACNGrFT8jAodskhKQhWVGNxhp2l4zTLTbUNfdEeFX/KKoGjmMgyX0HaSaRw5++dx4EnsX3Hvok4Dn/C4icPtakjSvLxsM/WoIumxs7j7TgnElFIduotnY43n4QakcDzEu/BWHiefH8WIQQkrayrSKWzyvHhp2etb+JiqlUZzccb94LYfrFEKcsTcxOCSExoUwaIWnojc0nja/tQaWByaKoGjiGweJpJbCaeJw/o7TXbXMyTbCaBeN7/W6sy68r3vUX9d3eOcMsYERBBlafOyZgXzqGEwBegOWyn1CARggZ1vSy94Rl0kyZ0CQH5Nq9CdkfISR2FKQRkmZkRcXaT08C8AyDtjlTFaSp4DgGxXlWPPLjC1CcF3nm2I3LJwAAuu0SAMAleYK0H10/M6pmIOGo9k5oqgLGnAnr1b8CXz4trv0QQshQwXqXCGthRp7Eg2EY8KNnQ6k/CE12J2SfhJDYUJBGSJrRyw2njsnDmBHZAdmpRGvpcOB3z+zE1qpGqKqncUgsCnPMAID2bhcAX6fHEQUZcZ2P5rbD8da9cH74JADQDB9CCAHAsHomLXH75EfPARQ3lPqDidspISRqFKQRkkZcbgWvb/LMRZs9vhBWc3KXlR473YETZ7rw9zcPQla0kI6OkXBsYAnOm5+eBMcyKMg2x3wumssGx4ZHoXY0QZh8QcyvJ4SQoUovd0xkow9uxCRAMFPJIyEDhBqHEJJG9lWfxfodngXiPMfCmcQsWpfdjSfXHgp4TA+6oqVvr6gaXG4FnT1uLJpWAjbG/chnDsH54ZPQbB0wX3Az+LIpMb2eEEKGMr2oIFFr0gDPul/TvKvAZBYmbJ+EkOhRkEZIGtm4u974muMYiELykuGtnc6Qx8aWZce0Dz0YU1UNHTZPyeO0Mfkx7UNTJDg/+DvA8rBedTe44nExvZ4QQoY6XyYtsfsVZ16W2B0SQqJGQRohaURRfbPFeI7FudNLkZNhwumWHry+qQaqqsWcpeqNzSEFfP/ojy/odV5Zb/wzaR3edWm5WaHz03SaywalqRrymUNQGo/CesUdYHgRlkt/AjanBAzfe9t+QggZrpKRSdOp3S3QHN10g4yQFKMgjZA00uPwdXKUZBUcy2LepCI0ttkAeII4lo2va2Kw/350IuD7WAM0wJdJU1QV9/9nHwAgLzM0SJPPHIJr24tQW04C0ACWA1dcCc3RBSarEFzBqJiPTQghw4Xxt1ZJfJDm2Pg4oEjIuPY3Cd83IaR3FKQRkkZ67L5WyP7r0fSui7KiQUjQ/9Wy4sva/fUnF8a1D/3C4Z2tp4zHcjNN0FQZ0pFN4ApGgSuuBCOYwbA8xHlXgRsxCVzRODBC7xk3QgghPiLvuTmnd/9NJH70bLi3vwzV1g42Iy/h+yeEhEdBGiFp4lRTd8BMNJfb9zXLJnaQKeAZkj1tTB6+etlkmMT4snN6uePx+k4AAAMN/OmdsO16FVpnE8TZq8AVV4IrGgvrVXcn7NwJIWQ4EXnPjbqkBGkVc+De/jLk2r0Qp16U8P0TQsKjFvyEpIk1z+0GAGR42+6XF2caz+nBkNPV/26Piqpi68FGtHe7UFaYicIcS9z70hezS7KKORlNuG/UBrg2/g0My8NyyY8gzv9cv8+XEEKGO8EbpLnlxHf8ZfPKwGQVQT5FrfgJSSXKpBGSJvTyxh98biYKss3I95s1lmHx/K/84d56XHdhZb+Oc+JMF/7+hmd4Kc/H34RE01SYGvaAhQoVLKZltsMsMBCX3Qp+3AIwMQ7GJoQQEp4oeKod3EnIpDEMA37UDEjHPoWmKmAStO6ZENI3ukoiJA04XJ7SxuuXVmJCeW5AgAYACyaXAABcUvR3Uf+1/ggOnWwLedx/HyY+vg9jtbMRjjf+AOv2pzFT9KxH+0SaDuvnfg9h/CIK0AghJIGEJJY7AoA4exUybryPAjRCUoiulAhJA61dnpllBTnmsM+zLIOcTBFuKboPaEVV8cHuetz/Qmj5it4dbNG0ElyyoCKm89Q0De7962B7+ZdQ2uvhnv9l7HOPBgBMnVBGwRkhhCSBvibNHcONuliwmQVgLbHNySSE9A+VOxKSBvRMmtXc+/+yJp6Lej3CW5/W9vqconqCtEvmV8TcMMS961W4d78BfvQcmM7/ClyyGRq2AABK8q0x7YsQQkh0BD555Y46qXo7lJYamBfdkLRjEEJ8KEgjJA24vOvRzGLv/8sKAhtVJs3ulPHapppen9eDNI6LfT0aP3oOoCoQ518HhmFh9huIrZfjEEIISSxR8JY7JimTBgBq6ylIn70L09wrwYjxN5QihESHrpoISQN60xCz0HtmS4wyk+b0a90fjuKdj6Z3jIxEU2VIJ3Z4XlM0FqYF14NhPH9aMi2CsZ3A0Z8bQghJBqPcMYmZNK58GqCpUBqOJO0YhBAfumoiZJDr6HHhsdcOAECf5Ycmv0zah3vr8dRbB8Nu53D3Hcj5MmmR/zxoshvODY/C+d6jUFrCZ+fKizIAUCaNEEKShedYMEhykFZcCXAi5PqqpB2DEOJDV01DQLfdjZ89thlvfnpyoE+FJEF9i834OjtD7HU7UeCMRePPrDuCzZ81ht3O6fJl0irLQheC60EaHyGTprntcLzzR8i1e2A67yZwRWN72dKzHwrSCCEkORiGgcCzkJIwJ804Bi+CGzERCgVphKQEXTUNAe/vOo22Lhde/fgEDtW2D/TpkATzL0809VnuyEa8i+qSFPz+X7sAAFlWAbI3IPMXTbmjau+A/c01UBqPwXzRtyFOu7jXbRXVs7++1tMRQgjpH7PIweFKXpAGAHz5DEC0QpPdST0OIYQahwwJsuK70N5a1Ygpo/MG8GxIounr0e7+yrw+t9Mzaf5BnappYBlfsNXUZje+Ls6zwOX2BFAuSUFnjwsAjMCtr3JHpfEY1K5mWC79EfhRM/o8L70zZXaG0Od2hBBC4pdpFdHj16wpGcSZl0CceUlSj0EI8aBMWpo7cqodb2+thSiwKMwxQ1aSV49OBoYepBXl9t1NS+RZOFwyHn3lM+MxKajbY2unZ97a8rnlyMsyG1mue/61Czf9eh0A35y0cJk0TfEEXMK4+ci48b6IARoALJlZBgDI6aNUkxBCSP9kWQT02FOT4dJUutYgJNkoSEtz/3jnMADgwlkjPfXoSmj5GklvesdGMcKargyLAJtTRtVJX8mrK2h9Qos3SLvy/DHgWcYIyOqaewB4sl4d3oxa8BoytbMJthfvhFznCQKjHWx69ZKx+Pv/W2rM8SGEEJJ4JpGDK4oxLP3l2vU6bP+5A5pG1xuEJBMFaWluZKGnc96Ny8eD51jISezsRAaGXs7KR+i2uGxuechjbr9Ojg2tNhyubQfHMsi0COA4BnLQ3dDb/vQx1u+oCzmeau+A/e0HAMkFNqswpvNnGCbiuRNCCOkfgWNTUk3DWHOgdbdA7WhI+rEIGc5oTVqac0sKKsuyjQthKndMX797ZidOnOnCjcvGY+WCCuPxaOeW5WaK4FjG6M4IAC5v0K5pGu5+YpvxOMMw4FjWyKT1RXV0wfHW/dAcXbBecQfY3BEx/VyEEEKSj+dZSCm4BuBHToMLgFJfBS6vLOnHI2S4otvbac4lqxC9Hf8EjqEgLY2dONMFAHhh43H84dldRimJomrgWAYM03eQxjAMvnjxhIDHahu7oGoavvPgRyHb76s+i06bG912d0gA+LMbZwPwttl/6z6oXS2wXPJDcMXj4v75CCGEJA+fomsANrsITHaJUfpOCEmOqIK0Rx55BKtWrcKqVatw3333AQA+/fRTrF69GitXrsSf/vSnpJ4k6Z1bUoy1SvpdtGTOSSGJZ3NK2HOsJeCxY6c7UVXTBsDTyIPj+g7QdBfNLccfblmEb185FQDw5NpDcLrksK35i3I8jUhOt9gCsm/XL63E1DH5nm8EM7gRkz1dHEdOjflnI4QQkhpCCpc88KNmQDlzmFrxE5JEEYO0Tz/9FJs2bcKrr76K1157DVVVVVi7di3uuusuPPbYY3j77bdx4MABfPRR6J16knwuSYVJ9GTSeI5FdX0Xbnngo4A27GTwckkKvv/QJ3j4v547kl9aMdF47nh9JySOEtDsAAAgAElEQVRZhayo4Njok94leVbwftuf9TYLCfYFb9Zt0/7AdQUZFgFKy0moHY1gGBbm826iAI0QQgY5nktd8zBhwmKI864EVLopTEiyRLzyKyoqwp133glRFCEIAiorK3Hy5EmMHj0ao0aNAs/zWL16NdatW5eK8yV+jpxqh90pQfR2zfMvWUtFhycSm//9vx347T93BDxmd/qC6eJcC5bPK8cTty9Fab4Vb2w+iVse+BDNHQ7wUWbSdNPH5RtfN7c7wm5TkmcFAGypagQA3LhiEi5fWIEF4jHY3/wDnJufiemYhBBCBg7vHcOipKA9PldcCdPsK8CIfY+GIYTEL2LjkAkTfGtcTp48iXfeeQc33XQTioqKjMeLi4vR1NQU04ELCjJj2n64KyrKCvi+uc2Oe5/fAwDIyTajqCgL/jfQqht7cMmi0ak8xUEn+D0baDUN3QCAwsJMY32Z4pfxaut2GuesZ0cBYH91KwpyzDH/PPd85zzc9dfNkLy/F3d+ZT4OVJ/FtMoCY18/vGE2/vyfvchgnJjQuQWlrTshHWuEqXwySq77CfjMwfUeDkaD7fcsHdB7Fht6v2I3HN+zfG8J+8G6Tiw7pyLC1qFifc9Upw3OusOwTpgX87GGiuH4e9Zf9J5FL+rujseOHcMtt9yC22+/HRzH4eTJk8ZzmqZFbGoQrLW1B6pKMzaiUVSUhZaW7oDHmtvtxteqrKClpTtgiOUjL+3F3Mp8RKOjx4XcTFNiTnaQCPeeDST/eTINjZ3GzLDGszbjcVnRjHMOnj/T2umM+eeRXBIA4I1PTgAAXE43rl0yFgCMfU0fnQsAmCrUo/D4ZmilE2G++FrwY+ej3cEAjsHzHg5Gg+33LB3QexYber9iN1zfs0VTivH8+iM43dgV888fz3vmPrABrk+fQ8YN94LNKYnptUPBcP096w96zwKxLNNn0iqqhS67du3CzTffjJ/+9Ke45pprUFpaipYWX6ODlpYWFBcX9/9sSdT8W6fr3R3dQSWO9/97T8T91DR04SePbMbX12zEix8cT+xJEkNAW3y/f6feFnlXFPf/TpPZm41ravME9PpMPQDQJCdcO1+FcvQTAMBe92g0XfBzWK+8C8K4BTHfdCGEEDKwrGbPfXeXOzXrxPiKWQAAuW5/So5HyHATMUhraGjAbbfdhgceeACrVq0CAMyaNQs1NTWora2FoihYu3YtLrjggqSfLPHxv+gXBc8/ozuoq+Oh2vaQ1/3j7UN45eNq4/seh2R8vW7bqUSfJvHyb4vslnz/TlIvQdqXVk7Ej66fibtuir+MJHiAdH62GWpPK1x734Lthdvh3v061LO1YBkGEngweSPjPhYhhJCBxTIMTAIHl5SaII3NLgabUwr51L6UHI+Q4SZiueNTTz0Fl8uFNWvWGI/deOONWLNmDb7//e/D5XLhwgsvxKWXXprUEyWB9Iv+olwz5k/yZDHDXfCrqgbW21CkvduFT7yd/K69oDJk20jDkkn8ZL/Mp8PtH6T5vi7KNRtfmwQOMysLje8zzLHPnedYBhwUKOCwfF45XNtfhnvvWs9zI6fBdM414ErGQ9j2EVySAoGnsYmEEJLOTCIHl1vBlqpGvL6pBt+9ejoqSpK3BoirmAWp6n1okhOMYI78AkJI1CJe+f3iF7/AL37xi7DPvfHGGwk/IRIdvdzxyysnoTDXs1jYHebumaSoMLGesje7y9dJUNM0HK/vxN5jZ43HyvzK4UhiKX6ZtA/31But9iXv47d/YQ4qR+aEfe29ty6GxRRbkKbJbrBHNuA3uW/i0e4VEPgKcCOnQhTNEMacAza31Nh2dGkWjtZ1wGoSYv2xCCGEDCJmgYNTUrDlQCOa2x2oa+5JapDGV8yC9Nm7UBqOGOWPhJDEiP32PBkU9Ba7nF9JW+XIHOyvbg3Y7uH/7sfPbpwDAPj0gG8elsOl4A/P7g7YtjDHDFlRQ8rkSP/5l6e2dPha4uvZzwyL0Gsmqyg3+hbHmqpAOroJ7l2vQ7O14bQyAqrGgOdY8COnhp13dsuV01DX3I1Jo/PQ2toT9bEIIYQMLqLgyaTpnzmqltwGbVzpRFiv/z3Y3LKkHoeQ4YiuxtOQy63g/n/vBRBYonjLldNw9fljA7Y9eNK3Lu1Uk+8CvNvhDtgu0yJgz7Gz+Pb9H1LXzSTwX5PW1uUyvtaDtESUGmqqCvurv4Hr43+AyciDePnt+Fv3CjSpuRD6mLOWl2XCzMpCoyyWEEJIejKLHJxuxajeSHKMBobjweWNpGZThCQBZdLS0ImGLuPumH/Wy2LiMW9yMV7bVIO8LBPau10Br3O6feWOOw83BzxnNfNGExG7S0amZXiWvqmahuc2HIXdKeObV0wBxybmPoa+Jo1lmLCNQ4QEZC8ZloUw6QKwmQXgRs/2PuoZVM3TejNCCBnyTCKHs51Oo6tvsjNpAKB2tcC16zWIsy4Dl1+e9OMRMlzQlVsa8r+gl4I6Oo4szMCTt1+EqaPzQl5XXd+F8iLPPIb1O+oCnvNvTHHwZFsiTzet1DR04YPd9dh2sAlnO50J2WePQ0JDq+cDsyjXHNB5S1+T1p9MmvvQh5BrPZlVcfrF4MfMAcMwAXc2RZ7r7eWEEEKGCJPAGQEaAGipqIzhBcjHNkOujTz2hxASPQrS0tDps56yxYrizLDNJliWwejSwIXCZ73roE639ODK88ag2y4FPG81+zJnb356MsFnnD7835fnNhzFnX/bgtc31fRrnz/48yd49NXPAAAVJVnotLnR2ePJcj67/iiA+IM09+GP4Prkn5COfNLndvMmFcW1f0IIIelDDPosSUWMxlpzwRaNpVb8hCQYBWlp6Jl1RwAAX7t8Sq9NPpbPK8esygKYxMDOjhUlmcjOEAO2ZQB02Xxr1KaNyYfDrxPkcOJfinjgRBuaOxx4fVNNwJqyWPz7vWMB3+d43/sHXtgLza8MJZ4gTTryCVwf/xPcqBkwL7ulz21zM00x758QQkh6Ce4EnKo15vyomVCbq6E5qfkUIYlCQVoa66vRA8MwKC/OhOxd86QHXZ+/aDzyswJnmWgA6pp9f1jX76jDbX/6OPEnnAbcUvhgrLapO679bdgZWFa6cGoJAMDpVuD2m2sX64w66egmOD96GtzIqbCs+D4YXgy73bdWT8Vvvr4gxrMmhBCSjvKzA2/IpWJNGuBpxQ9Ng1y3PyXHI2Q4oCAtjRXn9d2aXeBZKKoGRVXhcHkyRBYTj+nj8nHNkrH4328uNLZd5A0ecjLCX+wPF66gWXN68NQTVB4aDS3ow9EkcKgcmYOxI7IwotCKJ9ceBADcsGx8zJ2xlJaT4EZOgeWSH/YaoAHA4mmlGFWcGfO5E0IIST8r54/CNUvG4s8/OB9A6oI0tmgM2OLK5LeTJGQYoe6OacgkcrhgZhlMQt/NIPQSurYuF57d4CmRzLaK4DkWq8/ztOp/6Afnw+lWkJ9lwk0rJ2Hd9lNY612TpmnasGur6/Y2YsnOENFlc+OSBRV4e2ttwCBwSVbx6Kuf4bMTrXjo++cjyxo+SNI7Our0AFDgORw44WvOsmBKSdTnp0lOMIIZpnO/BChSnwEaIYSQ4UXgOaw+b6zxeZOqmIlhWGRc/cvUHIyQYYIyaWlmf3UrXG4FJjHyP53eBfKOv20xZnPlZQWWQmRbRRTnWsBzLKxmHlMqco3ngoOM4cDhUsAwwOpzxyAvy4RJ3vfDf41edX0n9le3QtOArVVNve4rOCun869snDGuIOTfpDfuA+/B9uJdUHtaPd0bKUAjhBASBuu9wZrquaeaqkKTEtMZmZDhjjJpaabR21p3/uTI2ZfgZhQ/vXF2xIHF2X4NJnocEg6ebMOMcQUhzUaGKptDQoZZwPJ55Vg+r9wYcWB3+oK0LruvyYrUR0MRlzt8kHb4VIfx9UVzR0Y8J01V4d75X7j3vgV+zFwwluyIryGEEDJ86SM+U1XuCACaIsP2/E8hTDofpgXXp+y4hAxVlElLM/pdscIcc4QtQ4O0cLPTgvmvSXv+vaN46q1DeOLNqhjPMn3ZnFLAzDiB5yDwbEC5Y6dfJ0ybs/e1as5eMmn+rKa+75Nozh441v0R7r1vQZhyEcwX3waGG56DxgkhhERnIDJpDMeDzR1BrfgJSZAhEaSd7XRgf/VZAJ4sx5mztgE+o+TRm1GwUawVE4IGGEezvizDzOPK88YAgDHMucc5fNrxO90KzEGBk0XksONQExTVkzXzb6uvN2QJx+YIH8BdtqjC+Hp8mDl3/lw7/gvlzGGYLvgazEu+CoalodSEEEL6xjAMGKRmTpo/vmIW1LbTUHtaU3tgQoagIRGk3f3ENjz0kqft6y+e2IZfPLltgM8oefTSBTaKfzmei73pB8MwuHrJOBTnWlDb6Gk7H6lBSTrr6HEFdGGUZDUkAykKHFq7XDhc2xHweHGuBc4+5sk98opngPWNyydgwZRifP+6GQB82bPLFlZELD81LfgcrFfeBXHyhdH/UIQQQoY9lmVCugwnG1cxEwAom0ZIAgyJIE3ymzfV00v2YqjQ74pFkxXzDza+sWpKTMfxL+8ridDqP1112934ySOb8ez6o8ZjkqwaDVd0371mOgDP79bh2nbjcbOJM9YI6lxuBfUtPcb2gGdUwq1XTcecCUUAgPNnjEB5UWav69FURxccHz4FzW0HY8oAVzyunz8pIYSQ4YZhmJSuSQMANrcMTFYhBWmEJMCQCNJ07ijWAKU7TY2h3NEv2DCLsWXD/ANfSy/rplySgjc/PQk5TPMMtacVqq09zKsGjz3HPCWyH+ypNx4Ll0nL9rbYf/yNKtz37z0AgNGlWci2ijjZ2B0QuD366mf45VPbISsqRpdmAQAmlucG7C8n04TffmMBCnNCg19NdsHxzh8hV2+D2tGYgJ+SEELIcMQygNZ7b6ukYBgGpoU3QJx5aWoPTMgQNKSCtHe3nzK+vu/53QN4Jsmj3xWLZnyZ/5q04MAjEsWvkD1sEKZpePqtQ3j14xMBbeilml2wvf472J7/KWzP/Ri2l38B94ENMR072dbvqMOuIy3417tHjMf00k5JCQ3SwgW4sqLippUTAQCf1Xhq73cdacaBGs/8M6dbAc8xKMmzwGqOromqpmlwfvwPqGdrYbn4O5RBI4QQEjeGTX0mDQCEcfPBl8VWvUMICTWkWvC3d7uMr/3bnA8lqqaBYWIvd4zUoCKYf2AWLkh7e0stdhxuBuAJbBwuGZv2N+C87p3QnN0wLfw8wDCQanZB7T4b07ETbc/RFkiKirkTi8AwwAvv+xp/FOWa0dLhxD/fOYxff20+JFkJCdJMYYI0RdFQnGdFQbYJXT1uaJqGR189YDy/taoR1fVdMZ2ndOgDyMe3QjznWvCj58T4UxJCCCE+LMOkfE6aTmk6Ds3RDX4MfZYREq8hFaTVedcCDWWaFl2pI+AL0iwmHlZzfG3b87NN6LaHrvNr6/J0fmSgorhpCzaeLsB/qzQUXXUFZi0tNboQijMvg+att5AbjkA+sQOm+deBEVOzzq3HIeFhbwMPnmNxz7cWBjy/fG45Xth4HLVN3TjV1A2XW4EYFKRxYbq06J0ee5wyNh9oxNVLArNeekYtWpqqwP3Zu+DKp0Occ0VMryWEEEKCsUxq56T5c+1ZC7X9NDJGz47qpjIhJNSQKneMNXORjlRVi9gRUKevSYuny+NPb5iNi+eVo7IsB3XNocGvS1JgZVy4Jet9VJxai9KeQwCA17eeCWkTzzCe81CajkOqeh+2l+6GfOZQzOcUDz2YBDwZwaffDjyu/3q7B17Yiy67hKLcyAHkzMpCAL6B1eu8pbYzxhUAAPZXe0ogv3ftjKjOk2E5ZFz9K5gv/IbxfhFCCCHxYlkmphb8Zzsd2OmtkOkvvmImtO6zUDvOJGR/hAxHdDWYZlRNizqTpm/Gc7H/M08bm48vrpiITKsApzu0IYvF3oifZr+FCXwTXpXOR0PFZQAQ0u3Qn2n2KlivuhsML8Lx1v1wV72f1PbAsqKGBJjBZbAWE49ffvUcAJ6sG8MA50wu7nO/931nMW5cPj7gsfd3nQYArJw/yngs2ypg7sSiiOepdJyBpspgTBlgMyIPHCeEEEIiYZnYWvD//l+78NhrBxKSfeMrZgEAFOrySEjc0j5IG6hU/kBR1eiahgBAltVT4vi5CyvjPp5F5OF0B84CU9rqsKrzeZg5Ff/SVmMfpsCteP4dXJJilAKGw5WMh/WaX4MbNQOuzf+CXLsn7nOLZM1zu/HUW31n7MwmDmNHZCMvywTAkwkrybP2+ZrCHEvYEkgAGDsiy/g6L9sc8Rw1Zw8cb94L50dPR9yWEEIIiRbDIKY1aZ09bgC+CpH+YDMLwOaXUyt+Qvoh7YM0fZjwdReOw4O3nTfAZ5NcH+w+jQ076wLa4/dF4Dk8fecyLJ5eGvcxzSIHWdFw+vhxyLV70dBqw4keKw5yU/BK5pdQUDkNLrcSEMhFOj9GtMCy8ocwX/Rt8KNnx31ufalr6saJM+HLX/2bqOiDulefOwYAwmYN/c2ZUBjw/d1fmWd8vXBqCaxmAZy3HDU3Q+xzX5rLBvs7D0Jz2SBOX9nntoQQQkgs2Di7OzpccuSNosBXzILSdhqa7E7I/ggZbtI+SLM5PX9MsjNE44IbiH0uWDr4l3fospLCbk0TSi1YbdmFrI33wLn137j7ia34w/P78C6WQDVlwSxycElKQHDjjiKIZFgWwoRzwTAs1K5mON57DJqSmA8GAGhuDyy7vGGZrzxx+rh8TBntKSvU7zIaa9MifKB9/7qZAd9XlvkCPr3UUS8v5fsYe6C5bLC//QDU1lOwrPgeuKIxfR6XEEIIiYWnu2Psr3tj88mEHF+cvQqZN/0ZDN/3DUtCSHhpH6TZvUFahlkA59cgg4uyuUa6kOTUD+pWu5pRvvthXGypwpms6bCuvguA5311SQrMIgeTwEFRNdgcvg6QkhTbp4Jythbyie1wbX0hYecefCfw4nPKUVGcCQAYV5aNr10+GRfOLkOlN6umd8LsrSlLeVEGpo3ND/uc3phF/53Tv+9rLaDjgyeMAC1Z2URCCCHDV6xr0gqyPWX/x04nZoQRI1rBcEOqiTghKZXW//fYnTK2HfIMUs4w8zAJHG5aORFVNW04WNs+wGeXOHc+uglVJ1pTeky1pw22V/4HYBj8w7kSGSNnw33GF4i53ApMAmfMEOvo8ZUzuGMMKIVx86FMXwnpwHpwZVMgjJ0X+UURPOPt4vibry/AKG9w9sPrZ6H+bA+mj/V0YPzqpZN95xAhSPvtNxaGfRzwtOiXFSUkg8b3caPAtPB6aFOWUoBGCCEkKWIdZm0SeQCusLNR4yVVb4N0cCMsq+4A08tabkJIeGkdpP31tc9QddITjOlzwJbNLUdblwufpTioSRZZUVMeoAEAm5kPcfblEMYtwN7HDgCfNWLzZ43G83aXDFHgYPaWmNY0+NZ/Rbtmzp9p4eehNB2D86OnwBVWgM2K3BWxNzanhIazNpQXZRoBGgDkZZmMBiHB9M+xaMcb+OM5Bi7JL4Pm3QcXlEnT3A5Ix7dAmHIRuLyRQN7ImI9FCCGERIONoXHI5n1ncOasDQAgK4ldUqE0HIHSeAR82ZSE7peQoS4tb2s0tduhaZoRoAFATqav5pnnGMiKBlXV0r77o1vyZaXKizL72DIx5NNVUNo87eRNs68Amx2+Hb2saMjJEANK+vRyv3iCNIbjYVn+HUDT4N7zZhxn7lPX5Gm7P39y9IGe/kEW7XgDf5wxj05/Lzz78G+morkdsL/zIFybn4PafjrmYxBCCCGxYFnGuAF5tK4D67adCrudpmlY88wO4/t4PsN7w4+eDfAmyMe3JmyfhAwXaRekVZ/pxM8f34rf/t9OlBdlAAB++40FyLb6B2meH+ub932AB1/YOyDnmSguv/VdpQV9t4bvL7WrBY73H4Nry/MBj4/0vs/B8rPNcPgFInpAHE3jkHDY7GJYV98J03lfjuv1uh7v+rhZ4wsjbOnHG5vF03BGz6DpP/+V540B4GvwogdoanMNzBd/F1z+qLD7IYQQQhKFga/ccc1zu/HiB8fDrlELDsr6GqMT8znwJvBj5kKq2QlNkSK/gBBiSLsg7bR3OHFtYzdOt9gwZ0JhSIbJP7tzKI3XpjlcMg57z784z4JrloxN2rE0VYVj498ATYV5yc0Bz/3KO+w5WKZVQHGexbcP79/+/jQ54QpHg+EEaC4bpJqdce3D5vR8EGRahKhfM2NcPi5dUIEvrpgY8/GuXzoeHMsYNwoEwfP7p6qat4vj/VCbT8C8/NaErLcjhBBCImHZ0HJHlxT6+Rx8Y1WSE1uBJIxfBLhsUOoOJHS/hAx1aRekBbefH1eWHbKNf5fHdPbs+iN4Yu1BAMDnLxqPEQUZmFCeg6WzyxJ+LOnABqjN1TCf9+WQEkeB55BpEcBzLH7wOV8LequJx/SxBfjfbwY21UhEqYRr12twvvco5PqDUb9G0zQ43bIxiDOWrBjHsvj8svEBGdloLZxagiduvwiid32eiff8V1E1KI1HobafgXnF9yCMmx/zvgkhhJB4dNsl7KtuDcie9dhDs1nuoMBNVtSYukJGwpVPAz9+MRhLVsL2SchwkFaNQ3ocErYcaAx4bNXiMSHbCX20Pk8np1tsxteTK3IBAD+/KfGZGLWrGa4d/wVXMQv8+MVht7n31sXec+oxHrN6Z4uNLMzAt1dPhdUs4KGX9sVd7ujPdM61UOqr4HzvMViv/R+wWZFLF59cexBbqpqMYdVCH3PKkolhGACeDzh+9BxkfvFBMKbwJaOEEEJIMrR3uwAAf3hut/GYM0wmTc+uZVoELJhSjI2766GomlHK318My8Oy7JaE7IuQ4SStopm/v1mF6jOeLoLXXTgOly8aHXa7oZJJyzB7gqAff2GO0b0yGRhrLoRpy2FecrM3wAhlMfGwmHiYRV9cbzH7vl40rdRYI9jW5ez/OYkWWFb+AJqmwLH+YWiyO+JrTjZ2B/y3rzllyeR2OnFz5seYJXoWaVOARgghZKAcP91pfB2u0sXtXft+82WTUZBjBoCEtuHXqV3NUNrqEr5fQoaqtArSDpxoM75etXgMPre0Mux2A3VxnkgHalpx+FQHZlYWYNk5FUk7jqbKYHgR5kU3gM3Ii7i9xa+EUM+kGd97g7bq+i4kAptTCsuyW6C2noJrx38jbq/fDZQVFQLP9hpwJpMmuTDtxDOYLdZi4Zjw7f4JIYSQgRBc2gj41nFnmHlw3llmwUtL+kvTNNjX3gvX9pcTul9ChrK0jGZ6y6DphkKQ9qcX9wEASvKS19FRPn0AthfvhtrRGHljL7NfYBb8PptFHsV5FjS02fvVPMQfXzEb5qXfgDjrsojb2p2+TpOJbCEcLc3ZA8e6P0JtPgbL8u/AOmtlys+BEEIICbZoWgmA8J+NXXZPpUqWVTRG6SgJnpXGMAz4cQug1B2A5uyJ/AJCSHoFaZkWAfMmFfWaQdMlqo56IBXlWDCiwBrxZ42X2n0Wzg+eAMNyYDIjZ9B0ejMOiyl8Uw6LyKOpzY7/bDyekPMEAGHi+WCtudBUBUpzddhtNE0zSjYGgua2w/7676A0V8O87FYIlQsjv4gQQghJAcY7Z+aPL+5DXXMPznY4jOdsDs8NzgyLYCwXSXQmDfB2edQUSCd2RN6YEJJejUNYBsiKoq36UMik2ZwSFk4tSUrzC9XWDvvae6EpEiwXfxcMH31ZHs+xuP875/YaCI8ry0ZtUzfaulyJOl2De9drcO97B9Yr7gBXOiHgOSVocPm4spyEH79PggV85UJwI6eCHzEptccmhBBCgvAcA9mbEWP9LiV+/fR2AMATty8Fx7JGdk3gWL9MWuJverIFFWBzR0Cu3gpx6kUJ3z8hQ01aRTOKqhn10n3h2fTOpB0/3QmbU0ZWHO3gI9FkNxzr/wLN0QXr5T8Fl18e8z4KcszIyQwf2N200jNnrLw4M+zz/SHOuARMZj4cGx6G2tMa8FxwFu2BH16Q8OOHo7TWQWmrA8MwMJ1zDQVohBBCBoU//2AJvnnFFADAvEnFGDsicGTRw//9DICvSYjAM8ZNbjkJmTSGYcCPXwSlqZpKHgmJQlpl0hRVAxtFAMYFZdI0TRuQJhKxsjsl/Pu9Y9jsHTOQZU1CR0dFAiNaYF52C7jixJdSMgwDnmOgqIm/C8eYM2G55Iewv/a/cLz7F1ivusvIArq9a+CuX1qJJbPKUtJ+X248Cse6h8DmlMB69a/S4neMEELI8GAx8Th3+ghMKM9FUa4FFpHDvc/vMZ7fX+252akHaVySM2kAIE5dDnHqcjDmxN/IJWSoSb9MWhTrzYIv0Fs7+98SPhVONHQZARoAFOdZErp/TdPAmDJgufz/QRgzN6H79sexbMIXHRv7zhsJy/LvQG09BecnzxiP67PZsjNEZEZREttfSstJON5+AIwl21MySgEaIYSQQago13MtkZ0RWp0jyQokRQXPMWAZJmndHXWMOZMCNEKilF5BmqIZd3n6ErzNB3vqk3VKCSX7BTaLppZg+tiCxO27di8ca++F5uxJekDBsUzS/sADAF8xC6bzvwJx+grjMcnbVjgVGTS1qwWOdx8CY86CdfWdYLOKkn5MQgghpD9K8qxYOrss4LFOmxuyrBmfnclsHKJTOxthf/33UJoS12CMkKEobYI0TfM0hogmSPNvHCIKLJraHX1sPXjIfq1xMxNY6qi018Ox8W/QJAfAJz/LxHHJDdIAQJx6EbiiMQAA6eRuuCVPdyqRD991MpFcu1/3NF259EdgrblJPx4hhBDSXyzL4CuXTsbTdy7DDz83EwDwykcn0Glzgec8n518klrw+2MsOVBaT8F96KOkHYOQoSBtgjT9oj+6IM23TZZFgNMt97H14CH7reOaNKJdtxMAACAASURBVCr6tvh90Zw9cLz7ZzC8CMvKH8TUyTFe3XYJH6Yoe6k0HoNz/V9g3fUMWKgQheT/SpvP/wqsV94FLn9U0o9FCCGEJJpe+rj1YBO2H2o2rpuMNWlJWFeuY0QLhPELIZ/YBs2dHjfRCRkI6RekRdFe3z+TZjbxcLkTM1g52fQ7V7/86jmYN6n/JXSaIsOx4RFoPW2wrPg+2MzElU+mwtG6Dhw82dbnNlzpBIgLPgdrw258LfMjmJTE/8HXNA3yqX1wrH8YmssGhhfB5Y1M+HEIIYSQVBhZmIEFU4qN72dP9Fxz6NdYchIzaQAgTF4KyG5I1duSehxC0ln6BGlKLJk0349lEXm4pIEJ0mRFRUtH9EGD3mEpt5f29rFSu5qh2TtgvvDrIXPF0sGa53bjgRf2RtzONPsKNFdeiWnCaRR+9L+QDn+ckONrqgz5zCE43r4fjnV/gtJ2GqqtPSH7JoQQQgaKKHC49arpWLV4NDLMPL53/WwA/mvSkpdJAwC2aCzY/HJIh6nkkZDepE0Lfj2AiWZQtX+54/H6TgCAS1JgEpK/Xsnf/71zGJsPNOKvP7kQJjHysfU7V70Nio4Vl1cG6/X3gIlittxgVtPQFTLfJVhL6bl4chdw+5QawGQFAGiKBIABw0X3a+4/qkG1d8D+0i+guXoAUwZM534JwpSLot4XIYQQMthdd2ElrlkyDqKgr0nzdndMciaNYRiIc66E5uyGpqlgmPS+TiEkGdLmitPh8qwrs5giBzv+JZFFuWa0dDjRY5dgykltkPZplaedfmuXE2WFGRG3jyUQjUQ6uhn82HlgBHO/9zXQTpyJHKS5ZRVNai7UC26DkOP5md1734J7/zrw5dPBjZgEJiMfbEauMR9OOXsSSvMJKM3VUJtroHY2gCudCOvqn4O15oKftARc8Tjwo2YMifeREEIICeY/f9a3Ji25QRoACJULkn4MQtJZ2ty6sBtBWuS4UvALclbOrwAAqFry/+D4+/RAA/RDdtncUb1Gn/XV30yaXLsXzg+fSFjZX6zOnzEiIfsZ6Q1s5SiGarq9Ja3+jUO4kvEQKhdBaToO16fPwbnhYdjf+aPxvGvHK3BtegbKqf1gsosgzrwMfOVC43nzohsgjJtPARohhJBhQS93lJNc7qjTXDZIRzdBS9HxCEknaZNJ+2R/AwDPGrNI/O8K6bM/1BTcFfK37WCz8bUSZYB4sqELORkihH60kdfcdjg3PQM2rwzC1GVx76c/cjLFqNYORqKXHkYK0jRNw+6jLQACW/Dz5dPBl0+HpmnQHF3Q7B3Q3HbjedOiG8Gc/2UwmYU0jJoQQsiwx6WgBb8/ub4Kzg+fhCWzAHzZlJQck5B0kTZB2llvA47RpVkxvS6VqXt//tmzaP/YVdd3YlxZTr+O69r6AjR7Oywrfjlg66dYhklIUKzHeZHev7rmHhw+1QEAEMK04GcYBow1B7AGvrdcXlnItoQQQshwxelr0lJ0zcRXzAJ4E+Tj2yhIIyRI2pQ7yoqKieU5UZU7+jMyaSkud+ywuYyvow1YnJKCknxL3MeUTx+AdPhjiDMvA1c8Lu799BfLMtDQ//dcf3WksouOHl9AzFJGjBBCCImLvtzigz31aGq3R9i6/xjeBH70HMg1O6Gp6THTlpBUGbAg7f1dp1FV45uB9cgrn+GXT/U+L0NSVPB89KdbVpiB7AzRaMKR6nJHh1PG1DGegdTR3JFSNQ1uSe1XB0o2qxD8xPMgzrs67n0kgl5u2t/3XH99X/NaNuyow0Mv7evXcQghhBDia7xW29iN3/5zZ0qOKYxfCM3VA+X0wZQcj5B0MWDlju9uP4XmdgeevtOzbkpfU9QbWdGQYY4+SPvt1xdAg4bPqj2BYCrLHTVNg1tWjfVz/vNG7E4JZ1rtqCzLDlgH9eSbB73Px38nic0phWXpt+J+faJw/kFaPxpqKkaQFj6Tpmka/v3+MeP7u748L/6DEUIIIcOc/3pyvat20o9ZPh0wZUBpPg6+YmZKjklIOhjQcsdYuhjKihpTa3qWZcCxrJHVsTkkdNuj67IYL1lRcfx0J5xuT6dBs3dcgH9G6eUPq3HPv3bhw71nAl679WATAKAjyk6QAcetPwjH+oehOXviPfWE0ksO4w2MX9x4HHc+vsUol9x77Cy0MKWT+6pbA74fF6FNPyGEEEJ6l6g5rbFgOAEZN6yB6ZxrU35sQgazAQ3ScjLEkMd6y5rIshrXHw/9rtAfX9yHH/5lU8yvj8UHe+pxz7O78O72UwAAs5FJ8wUY3XYJAHC0riPgtXoAeuOy8TEdU3M74Pz4aSjt9QAf+n4OBD0wDhdYRWJ3SljnzbKe7XACAM52Oo3unv46elwB37MJ6ChJCCGEDFeJmNMaD9bsaQoXz3UDIUPVgAZprV0uHDgRmA3pLfvilmPLpOkScd3e2eOK2Aa+2+7Gv9/zlN61eIMLS5hMmss7z6uu2Zf10jQNiqLiinPHID87+plcmqbB+fE/oPW0wnzhN8AMkiBND4z7WkvWm4/2+TKM/o1HWrzdPQPQ33JCCCEkYQZyHI1z0zNwfvjkgB2fkMFmwLs7/vHFfZBkXwCkhAmGzpy1ob3bhZqGrpj3H5xd6QzKvkQiyQp+/MhmPPjC3l630TQVB46eAQPPuXf32GFlnMaatLKaN2F79bewvXQ3Pt/1NH6X+yKuc79i3DGS3G5oAMQYGqMAgFT1PuQT2yHOvw586YSYXptMTW2ejlBPrI19EbDTpYR9/K0ttSHr9VI9VoEQQgghycJAPrEdmjvMTVlChqEBC9LMfkOp7U7J+Dpc9qX6TCcA4NzppTEfR5/5ofvxI5sDukpGsnF3PQDgSF0HmntpR+vc8Cimb/8fPJT/LB7Iexbf6n4EP8t+G2bR2zVDcYMRLWBzSlGPUux3j8IO5zjYXTI0lw2ul+7EFZbdMDPRr0fTZDfc+98BVzEL4qzLo35dKsyZUAgAaOtyoraxOyAIj+RAH/82G3bWBXwfy34JIYQQMnjx4xcBigS5ds9Anwohg8KAdXdcMmsEPtmr4VRzD378yGbj8eCyQqdbxj/ePgwAWDpnZMzHUcLM2GrucGBalK8/csq3duxEQxeK86wh28ijF+KdYywguyBCgUMT0KVaMNMbiFaPugqVCyoAAK/9fSua7J5g74oeNywZCrTiSVhh3wrp0Ak41cXgR80CN3IKGE7o9bwYXoT1yrvAiFYwzIAnRANMGZOPyRW5aOt24Tf/3IHzZ4zA11dNgSQrUDXAJHBwuGRs+qwBs8YXojjXNxuur2xphjnw19Ut+bJu37t2RuJ/EEIIIYSkBFdSCSYjH1L1NggTzh3o0yFkwA3Y1X1+lgmXLKwIeVz2lrB19rjQ0GoLWLsV6yBrALCFaWmfZek9+AnGMDCCiLau8KWSf98jYF33FKxzzMYbjnnY4JyJbe4JsHiDiv9sPG5s65YUFOV61p2dONMF1pIN14Kv4v7OVbBnjYZ0+BM41v0RWo93dEBzNezHdkFpq4NUvQ2OjY/D+dHTAAA2swCMGP/w62RiWcbocnn4VDsA4O4ntuG7D34EANh7/Cz+/d4xvPzB8V73Eez5947hiHdfgG99X3aGiLkTixJ16oQQQggB8Mm+M5E3ShCGYcFXLoRSd2DQdKsmZCANWCaN51gIXGhpo6Ko2HawCY+/UQUAuPUqT84rL8tktHaPxajizJDHot1Na6cTbV0uZGeK6Ha4Q7oJ6qpOtod93CJymFVZgH3VreiyuZGdIcLlVjCmIhctHU48/fYhrN9xCjcsn4DTSgHOTL8AIyfkQWk6DjanBADgPvAeGo9v8Z27JRtc+QxoigyGG7B/vohYhjHWF57tdAb8F/CMRACAtm7fexrN8OsNO09jUoVnSLg+w+V331yYmJMmhBBCiOEf7xzGklllKTueMPFcMBwPDRqoXzMZ7gbsKp/jWPB8uCBNC+j4WNvUDQD49c3z4zpOUa4FX1g+IWDosd5wQtM0yIoGIUzDDllR8f/++ikAYHJFLgSegxzlGqgRBVY0tNqhqBoWTy/FvupWPPTSPk+QJikoLbDiPFMpNh9oxOkWG056S/wEngXDi+BHTjX2ZV7yVWQtugztjU1gzFngRkwcdOWN4bAsA5cU/v1SVQ0Ob5bN5VeyqHdwZAD89hsLsOtIC17bVBPwWv+h53aXjJI8CzJjyIwSQgghpHffuXo6/vraAeP7U03dqCjJSsmxufxR4PJHpeRYhAx2A3a1z7FM2HkYsqrC//bJO1s9M8cyrfFfiAtC4I+peJuTvPZJDW554MOAtU06fZ4ZAJQVZkDgGEh9tOHPzRTxzSum4LoLx2FmZYHnnC0CsqyetvgnG7uxv7oViqrBLHAoLfCtbevo8TQMCRcsMoIZltHTIIybD75scloEaIAnk+a/vtA/SyYpqpEF63H43uczZ20AgG+tnoqRRZm48vyxve6/xyFh+6HmgAY0hBBCCOmf+ZOL8bmllcb31fWdKT2+piqQa/dC7T6b0uMSMtgMXCaNYYzufKX5VthdMrpsbkiyila/sjhdPKWOuuDW9rK3mcj6HZ5ugQ6XDFHgArZxy77A7UsrJuJATVvYzpNOtyfYmD+5BOdOH+HZv6JizoQiVJRkodse2rHRahYCAlQ9gyTyXMi26YoLGn3Q1u37N5VkFS5vJq3L5oamaWAYxliPGK5EVacPNG9u97xnU8fkJfS8CSGEkOHu8kWj8fKH1QBS30lZc3TBsf7PEGdfAdP861J6bEIGkwFLy7AscM6kYty0ciJ+8/UFRnc+p1vptUwuXnlZgQOiFVWD0y0bpXbOMJk0/Y/SrVdNA8MwEDg2bLnjR3s9i2oLcnzH4DkWE0flAgCyrCIe/9lSPHnHRcbz+dkmmPyCwv3VnvLOcJm0dLXLrywRQMC/qSSrRvZS03zlp3rmzX9o+f98bT5+dP0s43vO+5yedZs7iRqGEEIIIYlm3GxN8YBrNiMPXPl0SEc3Q9No1A4Zvgau3JFjwbIMls0th8CzsHhnijlcMiRZxfRx+ca2lSOz+3WsSRW5+OLFE/D7b3kaTCiKhsO1vtb6elbHnx6k6Rk2nmNDxgMAwOnmHphEDivOKe/1+ALPgmUY3HvrYiybOxKTK/LAhyttHMKrZP0DXElWAtaiHTnVgf/P3n0HSFWe+wP/njJ1e1/K0mHpRVBBKYKAICAqxqhEUWKLidFEY/td49VcEzXJ1ZurMXrVmMTYFQt2pFhQKVKldxbY3nennPb7Y/ru7O7M7Ozssvv9/JMp55x5z7sbnGef932er3ec8s9vcBauX15KyO+Cy61BNwx/77zs1NAAnIiIiNrvP5efBaB5a6REMA2bCqOhEtrJPQn/bKKuotOWO4pNlsP5yuu/+9VhVNe70TvbjhfumYUTZfVITTK377MEAbMnFaDB2zRb042QhtbOMEGaL9PjWyopy0LYf6ganCpy020QIoiwctJt+MncQgDh2wB01/1VZpMYsp/vj69sQXqyxf/8z69tBQBcc4FnbiQpNIAVBQECgCSbCfUOBS63BlEQYDaJSAu6DhEREcVHb+/e+XD79jua3H8CYLZB2ftlSDE1op4kokxafX09Fi5ciKKiIgDAvffei7lz52Lx4sVYvHgxPvvss+g/uElQ4wvETlU0wuFS/Uve+uQk+4tvtJcvQ7PrSCVOVTb4Xw8XpB06Gai4CAAmSYSiGThaXBeyn6zRpcIeQ/+2kQMzccX5Q/3P50/uh/zM5o2yuwPDAFZ8ccj/vKLWhYMnmzet9i179O07C/b8PbNw6YxBADw/L003YOumQS0REVFnEwQBZlmEO8F70gBAkM0wDT4bevlRGDqXPFLP1Oa33G3btuE//uM/cOTIEf9rO3fuxEsvvYTc3NyYP7hpYQlZEnH+xL74fLMnEOyI/Vm+why+PWBpSWbUNLj9xT+C+Y7J8i6nM8kSdhyqwIMvbsTPLxmDid69UI1O1d+cOhqiIGDumQWYOCwHZpMYt0C0K1JUHbuPhu8lFyzcnrRgVu+S2C+3n4RuGM2ysURERBQ/siRi6/5yXD5zSMI/23L2jwGT5bSpak0Ub23+5r/++ut44IEH/AGZw+HAyZMncd9992HRokX4y1/+Aj2Gv3I0DdKAkMr7MLXwRb09RFHA7ImBvWNJ3iWH4fak6YaBYX3TkOkN0lKCWgAEN7V2uJSYMmk+WWnWbh2gRSMQpIUPvvpme6o+rtt6ErpuhP0dIiIiovhwKRqKKxuhqIlf8iiYbRAEEYbigqG42j6BqJtpM7p4+OGHQ56Xl5dj8uTJeOCBB5CSkoKbbroJb775Ji6//PKoPjgzMwk5WUkhr/1obiFWeTNpWRl25OTEv3lidmbgM30VAmWz3OyzDABpKVb/6/nZgbLweTnJ/tedbq3Dxhqso6/fGZKsMhqcgSzmW+s8SyLz8tLCBmA5OSmYfWY/bN1XCpNJhskktTov3XHOOhrnLHqcs+hxzqLD+Yoe5yx64ebs6vkj8OIHu5CekQS7NfZ+tbHSHHUoeuZuJI89D1mzrk7457eFv2fR45xFLuoUUEFBAZ566in/86uvvhrvvPNO1EFaZVUjpCYZOBOAx2+dit1HKjF6UBbKyuqiHV6bjpyobvZaeVVjs89yOFWk2XX/63JQzFDlPV43DDQ6VUDXO2SsPjk5KR16/UQKDsxsltAgzaeyor7F8w1NR6NTRaPDDUM3WpyX7jRnicI5ix7nLHqcs+hwvqLHOYteS3PmcnkKrpWU1iE5TMGzRBD6jEHNd+9DKTgLUnrvThlDOPw9ix7nLJQoCsjKark3cNRrCvfu3YtPPvnE/9wwDMhy9Mv9stPCV+VLSzJj8qj8DvvHwFecwkeWhLB70tyqBpMpMD1pQRUmfeXj//7BbhgAMlJYYbCp3Axbs9d+fsmYkCqYSWH+KnfLxaNbva7ZLMKleMrwc7kjERFRx5G9/53VOqEMv4/lrMsA2QzX1/8OKdxG1N1FHaQZhoHf//73qKmpgaIoeO211zBnzpyoP1gSO2cj6LJ5hf7H9109ERaTFHZPmlvV/eX3AWBw3zT0yfEslfQFaVsPlAMAJhbGXkClu7pu/vBmr00Ymh3SCy7J1jy4bys4t5okaLoBRdVZOISIiKgD+VriNP0DdzwcK6lDvUNp8zjRngbLpEuhnfgB6pHv4z4Ooq4q6khp+PDhuPHGG3HllVdiwYIFGDFiBBYuXNgRY+sQdqsJl04fhPuXTcKQPmmwmuWQEvwHTtTgryt2wOXWYJIk/+u56TY8tPwsCPAEaYZhwOnWsGBK/05bAtCVhftjlygKIZUbw2XSLGap2Wsh73ubize6VAZpREREHciXuXp19YG4Xre20Y3//PtGvPTp3oiON42cBTGjL7Tj2+I6DqKuLOJ1iqtXr/Y/Xrp0KZYuXdohA0qEhecM8D+2mkMzab//12b/4+DljoC3Z4hZgsutw63qnl5d7ajs2J219De3X/94PO5/7jsAgeqawXxBWEvM3iDO4VRhbuNYIiIiip2vR9qmPaVxvW5FjRNAoCdtWwRRgn3RPYAlqe2DibqJHt98wmKWwu5JA8K3AbCYJLgUDQ3eFH17yu93Z3oL68b7ZCf5l5EmWZvPXVtBmu99h1vlnjQiIqIOpGmtL3PcsLsER4ujLwThW8HU1uqZYII1GYIgQK8pgVZ+NOrPJDrd9PggzWqWUNuohA0qzKbm02OWRXy57SSq690AgPRkFg0Jp2+2569dS2YMgiyJOHN4YN+eb217Ra3nL2nBRUba+gfb6gvSuNyRiIioQ2lBVbifWrEDv31+Q8g+sr+9+wMefHFjVNdUVA0vr9oHACF7/yNhGDocn/4Fjs+ehOF2RHUu0emmxwdpZlnC8dJ6vPLZfpRUNoa8Fy6TZrfKMAAcPFkDAEhLZiPqcNKSLXjhnllYMGUA/vrr6bh58Sj/e7+5cgIAYPyQbEwf1wu3Lhnrf88SJjAO5l/u6NKYSSMiIupAwZm0zXvLUFRWj2Lvd6UGZ9tFP8I5UlyHE2WePrVmObptC4IgwjJtGYz6cji/fimmzyc6XfT4tXq+rM63u4pRWecMee9kRUOz4y+bMRj//fo2lFZ5/oIT7V+BeiK5SbA7rCAdj//iXKQmmXHWiLxWj23KZg78yjJGIyIi6jjhVqz4toh8suFYTNcMLtaWkRr9aiQ5fxjMEy6C+/t3oRSMhmnIlJjGQdTV9fgI48LJ/QAAIwdkorbRHfJeZa2r2fGp3n5pVXWe92QGaTFJS7aE9EzzCfdaMJsl8Fe3NC41JSIi6jBzzixo9prT5QmyfMHWhKHZUV0zuFib1MZ/81tiPuMiSHlD4fzyH9Bry2K6BlFX1+MjjMJ+GUhLMsNmkVHXoCDFHqg4eOXsoc2O9+2Z8hUOCbckkjqONSiTlp1m7cSREBERdW/hink5vJk0xVv5UY+yh5qv1ywAaDE2pxZECdZZN8FUOA2CPTWmaxB1dYww4Flip2o6ahrdyAjKzgQXtPDxFa7Ye7zafy4lTnBFyJz05j8fIiIi6ji+DJpb8QRpahtB2t5jVThR7tk+crK8Ac9/sBuApzp2tAFeMDElG9ZzlkKQLTCCCpwQdRc9fk8a4Fmy2OhU4XJryEix4FhpPQBAEsNUd2zyVyUGafFx0bkDIEaw7MFsknDNvELsOlyJUQMzEzAyIiIi8nll1X5s3FOKA0WeAmqa1nqA9OjLWwAAL9wzC396dYv/dYdbxYbdpSjIPYIFUwbEPB69pgSOT/4HlmnLIPcqjPk6RF0NIwwAJklATYNnP1qKvfVqjU1LxMsSq1fEw8XTBuGiqQMjOva88X1wyyVj2P6AiIgoQZ65c4b/sS9AA1rPpB33/tHbx7dEcsqoPPhWOq7fWdyucQn2NBiGBufqZ2A469s+geg0wSANnmyYb410r2w7+uQkYeE5A8Ie2zTbw8IhRERE1N2ZWiiX31rD67fWHQx53jcnGdlpVlx34YjA+e1Y8ggAgskK26yfwXDUwPnF32HEuM+NqKthhAFPxUZfj7T0JAt+99Ozcen0QS0e/5/XnYnCgnSMHZwV0RI9IiIiotOR3RLYGROukqPaynJHXwCWkWKBqulwqxp6ZSWFbBXxZdfaQ8oZAMuZS6Ae2Qxlz7p2X4+oK+CeNAC9s5Ow/WAFAECKYPliv7wU3L30jI4eFhEREVGn+sNNk+FweSo63rpkLP792T58vrkIAJCVasHx0nqUVDUiL8Pe7FzVG4BV1bnw3MpdcCk6MlND8wPxCNIAwDR2HtSinVAPbYBp+Iw2W/oQdXXMpAGwBe0zC1cshIiIiKgnSrGbkRsUgJmDtnlUePvJ3vvMt2HPDc6ylVU74XJrzcr6xytIEwQRttk/h23+HQzQqFtgRALAGpTKZyEQIiIiovCCq1xfv9Czt8xqDr9fTQkJ0hxwutVmx7qD+qaFYxhGxPvMBEsSBFGC7qiFcnhTROcQdVUM0gDYzMFBGqeEiIiIKJzgTNo5o3thwZT+UFQ9bCAVXFSk3qGgwakiyWoKOcYAcLS4rsXPe/yNbfjpo2uiGqN70wo4P38aWuWJqM4j6koYkSD0L0CSyEwaERERUThNvyfZLTI03fA3tw6mhCkq4lve+JfbpuHuqyYAAB58cWOLGbWdhyqjHqP5zEshmO1wfvECG13TaYtBGgCbhZk0IiIiorbYrJ7vTHPPLAAQ+EO30602OzZc5cezR+YBAJJtJgwtSPd/7zpaUocjxbUtfq7L3fqyyGCiNQWWKVdCLz0IZdfqiM8j6koYkQCwWgKZNBP7nhERERGFNWVUPi47bzAumeZpVeTrnxauAIii6pg+rldII+z++Sn+x6IgYNm8QgDAH176Hg+9uAmarmPTnlIsf2Q1ahrc/mM37CmJapzykCmQ+o6Ga+Ob0OsrojqXqCtgRILQPWk56dZOHAkRERFR1yVLIi6c3B8WbwbN98ftb3Y1D6JcigabRYZJljBqYCaWBzWx9mla7bG82ok1Wzx7yXYcDARXf/9wD+odSsTjFAQB1mnLIPefAIjhC5sQdWXskwYgP9OO+Wf3Q7LNBHuTDa1EREREFJ4BT3GQFV8cwqJzBvhfd7hUuBXdn2m748fjw57fdAVTvVPxB26fbjwOABDgKTBSWetEsi3y72liSg5ss26K+HiiroSZNACiKOBHM4dg/uT+nT0UIiIiotOGqoYvj7/ii0MAgG0Hyls939co28fl1uBrc1Zd7+nDducVngDvjTUHYhqjXlMMx6f/C93R8p43oq6GQRoRERERxSS4OMitT3zhf+zwFhJpdLa+RLEgNznkudOt+bNr9Q4FNovkL1byw5EqAEBptSOqMRqqG+rxbXCsfIT707qokspGLH9kNbYf5M/Hh0EaEREREcVkUO9U/+MGpwpF9VRhHJDvef3a+c33oQXrk5OMFLsJdm+lbZdbg1kOLugmYUB+KmRJQP/8FOw8XIF7/vYNNuyOvJCIlNUPtvl3wFVdjspX7oNyaGPE51JiHD7lyXKu33mqk0fSdTBIIyIiIqKY9MtLQX6m3f+8rtGTOauscwIIrebYksdvnYr/uuFsAJ5S/npQY2xZ8qx9HD0wC4ZuoKi0AQBw6GR0Sxfl3iPwh6oFOKWkwLnqKbh3r43qfOpYZu8+xHD99noqBmlEREREFLPgCo0PvLABNQ1ufPTtMQCAKYL+s6Ig+CttOxUNuh4I0jTvY0kSoOqGf7+aEX4rXKsq9BT8T+08iBMuhmngpOgvQHGlajpe/Gg31m45gSff3gHAUxGUPBikEREREVHMTKbA18kGp4pf/+9X/ueyLER0DbNJhADA6dL8gRkAeItHwiSJUDUdgjdK81WVjJYOEe7h8yFYk2EoTji/eJEFRTrJwRM1+GLbKfzzk73+13zFYohBGhERLQ9GpwAAIABJREFUERG1g7lJGf3g8EkSI/uqKQgCTCYR3+8r82fSJFHA9HG9PY8lAZqmwxfyxZJJ83F6i5ro1cVQ9q+H45P/gaEzg5NoZlPz/nWnKhqx/WDrFUF7CgZpRERERBSz4EIfwSYMzY7qOpIo4kR5AxqcCgpyk/HMnefhkumDAHiaaKtaYLljjIk0AIDD5QnIpJwBsM5YDr30INzbP4r9ghSTpsH9Txd4iszsL6rpjOF0OQzSiIiIiChmTRtS+0Tbf/a6+cMBAKcqGyGKAkQxsFRSFj3LHU9WNAIANu8rxcnyhoivXVHj9D/2VaAEANOQyZAHToJ70zvQKk9ENV5qn+Bs6MgBGTh3TC/kZthQWhVdi4XuikEaEREREcWsaUbEJ9Vuiuo6qUlmAEBtgxuSGLqXTZIENDhVrN3iCaSq6934j+e+i/ja+4qq/Y8VLbSCoGXqNRDMNri+fSWq8VL7BFfx9BWfyUmzojwooO7JGKQRERERUczkFoK0FLs5quv4eqUZRvMS++MGZ8U2OC9V1YMeh66VFG2psM69Fdbzrm/XZ1B0QoI0sydIy0qzoaKGmTSAQRoRERERtcPMCX38j29YNNL/2GoOv1etJTZvkBbO8P4Z/se/XDIWl3r3qrncnqWLum7gpU/3Ysu+Mmh6815balD2rGkmDQDk/GEQ7ekwDAOG6o5q3BSb4B+TL5OWmWJBbaMS9mfY07T8/wYiIiIiojb0zk7yPx7eLxBMCUJk5fd9kmwtfy0VBAG3LhmDvAw7emcnwal4KjSW1zjQJycZB07UYPX3J7D6+xO4bv5wTPNWhfRRgjJpwXvSghm6CsfKxyBm94f1nKVRjZ2iF5xJK6n07DX0BfYutwa7tWfnknr23RMRERFRu8iSCJtFwiXTBvqXLMbCam793AlDc/wBYVqSBQBQ26gAQEgD7OoGTybs4X9twl1PrwcQmj1TtfClIQVRhpjeG8oPq6FXF8d4FxSp4J+Z05sR9S179D3vyRikEREREVG7PPWrGVh07kCYTSJGDczErUvGdOjn+YqSfLbxOABg5+FK/3tOlyfLdvBErb8IRXBgFpxVa8o86RJANsG14fW4j5lCGUGZNN/Pxxeol1VzXxqDNCIiIiKKC0EQcMePx2PC0JyYzr985hAAwNg2CoXkZdoBAGXeIhO1jYF9ZE63hife2BZyfMieNFXHifIGrN95qtl1RXsazOMXQD3yPdSTu2O6B4pMcCZt3tkFAIDMVE+GdM0WtkPgnjQiIiIi6hLmnd0P887u1+ZxsiRiyqg87C+qwbqtJ7BhdwmyUq2QJQElVY3YdaQq5HhF1b0NsXUomo77veX7zxndq9m1zWMugLJ7LdzbP4Hce0R8boywYu0BZCebYbPI6J+fAl+Mds/SMzCsIB0AMLRvOmwWGXo7mpV3FwzSiIiIiOi0YzHLcCsa/vHxXgCALV1CapIZ+45XNzu2wakgNcmEqloXikrr/a8bhtGswIkgm2GbfwfElOyOvYEeRNN1vPD+D/7nf7tjhr9wiNikJ15OmjWkZUJPxeWORERERHTasZhEOJVAgQmnW0NBbrJ/f9OQvmkQ4AnE6hsVJNtMMMkivt9X5j9HayFlI2X0hiCbYahu6M66Dr2PnqDOW+DFp6TK4V/uKDYJkk2y2GIFzp6EQRoRERERnXasZhluJZBx+dHMIeifl+J/npZkhgHAreqodwaCtODArLUiIoauofHtB+D66l8dMv6e5PU1B0Ke19S7gjJpocd6gjRm0hikEREREdFpJyPF4n98wVkFOHN4LgqCgjSbt1KgS9H8mTSpydK6cI2tfQRRgjzoLKiHNkAt3hfn0fcsRaUNIc9rGtz+ZtbNM2lSqz+XnoJBGhERERGddrLTrP7HvoCsT3YSLp46EACQl2kDALgVDfUOT5DWdHFjW3ufzOMvhJCUCdf6l2EYDBxipek6bBbJ//zVz/fjaIlnGWm45Y5uZtIYpBERERHR6SczNRCkTR6d73+86NwBeObO85CT7gnSnC4NjU4VyTYTzp/YN+QabS2rE2QLLGddBr38CNR9X8dx9D2LphtIsZv9zxucKlauPwIAEMRwe9IYpDFIIyIiIqLTTm6GDbMn9sXvrj8bud6ADPD0ajPJImwWT3btzXUHYQBItpkwsFdqyDUiCQbkIVMg5g6Gemxbm8dSeJpmICXJHPY9uWmQJjFIA1iCn4iIiIhOQ6Ig4Ko5w1p8f3g/T++tyloXAE+Q1ivLHnJMJHufBEGAfd6vAEtSO0bbvTU6VUiiAItZCvu+puvolZWE2noXRg/MCmlW3fQck4lBGsAgjYiIiIi6IZMsoV9uMuocnvLvFpOE7DQbfrlkLJyKimff2xVxMCBYkwEAemM1YBhATkobZ3RvqqZj77Fq2K0yjhTX4V+f7EVhQTruXnpG2ON13UCy3YxHbz4HAEKDNFOTIE0SWTgEDNKIiIiIqJsyySKcbhUAIEmeZXXjh2Zjf5Gn4XU0GRtDdaPxrd9C6lUIDLgn/oM9jWzcXYr/W7kr5LV9RYEm4k2bhGu60WxZo0+zTJosQlEYpHFPGhERERF1S54gzdMYWQwKEkyy5ytwNEGaIJthGjkL6qGNcBbtie9ATzM1De5mrxXkeLKNx0rq8NNH12D3kUr/e6puQJLChx3hqjvqhgFN79mBGoM0IiIiIuqWTLIEb89kSEFdk03egCHaZXXmsfMh2NNRseofMIymBf17Dl92Mlijy/Pa0WJPaf2/vrPT/56uG8161AHA/csmNXstlgC6O+JyRyIiIiLqlnxf+AGEBAmBQECL6nqCyQLLpEvh/OIFCAe/g2nI5PgM9DTjC8gA4MLJ/QEAH357FJ9vLkKWtzVCgzNwjKYZ/uWmAPDozVNQVedqVm0TAMyyZ/mjouqwhi8I2SMwk0ZERERE3VLLQVogEIiWPGwqLL0GQ6841v4BnqacLg0CgKljeuG88b0xbVwvAMC/P9sXkp3cebgCdz29HrphQA5a7piTbsOwgvSw1/b9zN5adwgOV/OMXU/BTBoRERERdUshQZrUvj1pPoIootfVv0NFdfN9WT2Fw62iV3YSli8Y4X9t2bxC/OPjvSitavS/9t+vBXrLhVvuGI5vKeoX207CLIuttlnozphJIyIiIqJuySQFZ9LavyfNRzRZAABa+VGoRTvbOLr7cbpU2JpUZfQtc3xr3aGw5wwIs7QxnODA2lf0pSdikEZERERE3ZIjqMBFe6s7NmUYBpxf/QPONc/CcNbHPsjTUKNLg80SuiAvPcUS9tjJo/JwxrAcTBqRF9G1ZZnhCcAgjYiIiIi6qXpvI2sAIX26RFGAJArtCtIEQYB16jIYzno4v321XeM8nbgUDSfK6pGXYQ95PbOFIO3ymUPwi0vHtFiCvylzUJC2eV9Z7AM9zXFPGhERERF1S/WNgSCt6Z4oSRSw83AllswYHPP1pez+MI+7EO6tK6EOmQy57+hmx2zcU4pUuwmF/TJi/pyu5HhpPdyqjpEDQ+8nOLN23fzhcLhUFPbLQHpy+OCtJcEFRnpy4RBm0oiIiIioW0qymfyPrU2W52m6gaPFde3ux2U+4yIIaflwfvkiDMXV7P2n39mJR1/e0q7P6EoUxbNPzN5kPoWgptTTxvXG3LP6oX9+StTXlyPMuHV3zKQRERERUbd0w8KR+G53CUYPzERyUMAGAFfOHoqXPt2HmnoXstNtMX+GIJthnX4dtBO7ALH7BxiK5mniHS6YspgkuJT2FfuQg6pwDu4TWbGR7ohBGhERERF1S6lJZsyZVBD2vUxvNcLaRqVdQRoAyL0KIfcqBAAYugpB7Bpfsb/cdhJ9c5PDNo2OleatiBkuSPvvX5wLwzDadf3g62pa+651Ouv+4T4RERERURO+jI2uxy8Q0MqOoOG1e6GVHwUAKGrHlpDXdB1/enULdh+pxMffHcPf3t2JmgZP/7Z6h4K/f7QHT63YEdfPVPxBWvO+ZzaLDLvV1Oz1aARXd4znz+Z00zXCfCIiIiKiBBK9e6g0vX170oIJthRA1+D48E+wLboXjebsuF07nNoGBbuOVOFYST0UVYdL0TCodxrmnlmABqenaEplrQuNThV2a3y+9vuyWx1VKj+4CqfWg4M0ZtKIiIiIqMfxVXuMZ7ZGTM6CfeFdgCDA8eEf4awsjuk6DpeK3z6/Aa+t3t/qcb6sVr1D8e8Fe/Xz/dANI6Qgyi+e+CKmcbT2mXIH7b+TJREWuDFUPoV8/WS7l092VR+sP9rq+wzSiIiIiKjH8TW3jneyRkzLh23Bb2CobljWPoF0sSHqa5RWOVBUVo9PNhxv9bhDJ2vCvu50ac2qVsarnL0vGOyITJphGJAkAXlSLX6R+hmuwbtofOd30GtL4/5ZnUnXDXy182SrxzBIIyIiIqIexxekdcSSOimzAPYLfwNn+kDU6p6iJA//a1PE5wcHVE632uLetmff2xXy/JoLPMVLfvHEF/jdP0I/z+mOz/64d748BCC06XR7GYYO16a34fry7zBJIoq1NPxv7Vy8q02HXluCxhUPQStuPat4OnEpWptFURikEREREVGP0xHLHUOunzMAZaOugg4RaUIDxld9ho/WH8Q/P96DrQfKW90L53AHgrRb/vsL/Pb5Dc2Oeferw81es5ilZq8N6u2p7OhuZ2l8H7MsITvNGtK8uj0MXYVzzf/B/f17MHQDJknAf908HbnDx2OzNgxJF98PWJPg2vhmt1n66I6gNx8LhxARERFRjxMoHBK/L/6nKhqwYXcpLjp3AARB8GdLhpmKMd26F7u+fx7v1J+HtVtP4tr5w1FUVo/iikb8+sfjQ67TdGliSZWj2WeFC9LCZWdG9M/AoZO17e5f5qNqOiYW5sTlWobigmPVU9COb4f5zCUwj18IQRCQnW6DxSRB0wyIafmwL7oXgmSCIAgwdB3Cad6PLpKfRUR3WF9fj4ULF6KoqAgAsH79eixatAhz587F448/3r5REhERERElWGBPWvyCtCff3oF3vzqMqjoXAE9AAwC7xEJ8Kp2H4aaTuDnlc1jgKZO/alMRdh6ubHad51bubvZa03GaTYGv8WeNyMWvLx+H/Cx7s/N8Tbyb7lGLlaLqMMvNM3bRMgwDjk+egFa0A5Zp18IyYREEIVDZURIFfwAt2tMhWJJgqG44Pvoz3Ds/a/fnd6ZIspptBmnbtm3DlVdeiSNHjgAAnE4n7rvvPvz1r3/Fhx9+iJ07d2LdunXtHiwRERERUaJ0xHJHXxzV6PRkwlTvksb0ZAu2GCPwz4ZpGCiX4tbUT/HWx1v85/mOb01do+J/rBsGBAQCmpsXj8boQVkY0iet2XlZ3qbd8VjueLS4Dm5VR3lN88xepPTqYhiqC4IgwDxuPmxzfgnziPOaHSdJQvOfjSBAMFnhWv9vuDa+BcOIX/uERHIrbY+7zSDt9ddfxwMPPIDc3FwAwPbt29G/f38UFBRAlmUsWrQIH3/8cftHS0RERESUIGIHBGm+PWF1Dk9A5Vt+aDVLUFUdW9wD8Vz9TKgQ4TYC2ahvfmi7VP+BIk8lR0XVsGlPaYtL5h6+4WwU5Cb7n+dmeAqX7DpaFcMdhdq4x1Nlccv+8qjPVaqK4Vj7HBreuBfKrjUAALlgLOQBE8IeL4pCs317gmSCdfYtMBVOh3vL+3B8/AT0xuqox9LZIgmY29yT9vDDD4c8Ly0tRU5OYB1qbm4uSkpKoh5cVlZy2weRX05OSmcP4bTDOYse5yx6nLPocc6iw/mKHucsej1xznTJEyTZkywx3X+4c6xmz1fr51buwrQJfdAvz1O0IzXZguoGzxLHXUpf7FL6ABBghoKBchk+3WTDFfNG+K+Tm2nH6EFZKMhLwT8+8FRwfGrFDrz1yEK88P4P+OBrz360/3fdWeiVnRQylpycFEzcV47jpfWYObEvxg7PAwCs23oSN18WuvctWr3zPJ+Tk2GLeM6UmlJUf/UWjm9fA0GUkHbmAqSdPQdycuvnpyZboetG2M8xlvwStZuHofLzf0Jd8zR6L3s4ZKlkV3ekrO22DFEXDtF1PWQSDMOIaVIqKuo7rJpOd5OTk4KysrrOHsZphXMWPc5Z9Dhn0eOcRYfzFT3OWfR66pxV1zoBADW1jqjvv6U5UzVPhqSqzoX3vjiEn8wdBgAQBaCixhl0pOe78yzbD5hv2451SilKS8+GIHgWublcKjRVw6h+ocsX1244ir1HA3vYBuUmQRCEZmO5YGJf9Mm0YWJhLqqrGnHu6Hx8t7uk3T9ni3cN3rILCiO+VuP7T0ArOYjUMy6AVjgHelIGqhwAHK2f73Qq0A2gpLTWX+QlRP+psF86BIbqQnl5PXRHLYzGGkhZBVHeVeKVVbQdpEVdGiU/Px9lZWWBDykr8y+FJCIiIiI6HcS7T5puGCipDN2rpXqXO1pMgaWNqXYT7vBWc5THLMDR1AmYYdoO55r/g6F79qYpqg6TJCLFZg65XqNLxaBenuzcf11/douJEotZwsTCwPfzrDQrVM1od5EU31z5ipGEo9dXwL3tI+iOWs9Ypi5D0hWPIvuCn0JMyoj4s3x7BlvrJyam50PK7g8AUHZ+hsa37kfjx49Drytr8ZyuIC7LHZsaN24cDh8+jKNHj6Jv375YuXIllixZEtMAiYiIiIg6gyx5chVKnErTf73jFOodSshrr37uacAc3L9MEASMGpiJF+6ZBQB4+wsZ24uBRQe+gcPtgG32LVA0HSZZhMUs4dnfnId6h4JfP/k13IoGs0mEIAC9s5MiHpvZGyQqih62l1qkfEGeL8D1v15fCfXQBiiHNkIvPei7UZjHzoOU0Tumz5Kk6PYMmsfOA2QL3FtXouHN+2Gddi1MQybH9Nkd7dXP9yOplUAXiCFIs1gseOSRR3DrrbfC5XJhxowZmDdvXsyDJCIiIiJKtCSrDLtFRnGYHmSxKG3hOmZZhM0c+Mp98+JRIe+bZAkrnWNwyezR0La8C62+ypNJkz1BpCyJ/kycS9Ghqob/vUj5z1e1VoM0w1kPrfI49JoSQHFBHnYORGtgT5ivmGJwkGa4HWh47R5Ac0PM6g/zmZfBNGgSxLT8qMbYlOTvY6cDaDuwFCxJsExYCNOQyXCufgbO1X+DoTjDVo7sTOt3nkKDU41fkLZ69Wr/4ylTpuC9996LfXRERERERJ1IEASkJJnR0CT7FStdNyBLor83ms8Zw3L8S/cAYIB3uaKP2RtwGUOmI2nYOahVJAjQMbBxJwytAIJk8gdZbkXzL4WMhu98h0tFqt3c7H21eB9c370OveQggEDmSupVCFhT4N6zDurBDeil2DHHqsJ0wAWX4IBl0qUQzDZYZyyHlDMQYlpeVONqjeS9RzXK5ahiSjZsC++Ca8ObkAvGxm088bLnqKca5eSRrc9V1Jk0IiIiIqLuwCyLcWvyrOkGJFGAGrR68rr5wzFhWA5Wby4K+cymYwAAt6rDnmzDug2HMdJ0AoOOrkHD61/AMvkKyAMmQpZEuBQNiqZDjjKTlp3m6ZVWXuNEXoYdhq5CO7ELgjXFE1xZUwHVDfPExZDyhkBM7wXBZAXMNv81DLcDqVVHsNDeAGzZAiUpA6YRMyEmZXTIssL2tEgQJBOsU64EABi6DvfGNyEPPhtiVr9OrwKpeIP4xdMGtnocgzQiIiIi6pHMJhFuNT570nTDCFkGeN/VE/3NpX1hRv+8lGZBgkn2Zsm8wWJxZSN+UAqAWbdB2PoWnJ89CanPKPSzjURxZSOKKxthkaPbV+brlbbqu8MYUrUeyvaPYbjqIQ89F7aZN0BMz0fSkodaPN88fAbMw2dg49YTePXjnfjDdeOQnpvrr0bZESIpHBIJvfoU3LvXwr3tQwi2VMj9x8M0/DxIuYPiMcyo1TsUDOqdCklsfe4YpBERERFRj2SWJX9w1F66biC4nkZaUmBZYV2jp0faOWOa79Mym0ILmCiqjj45SUgZMgHGoLFQdq2Ga9PbuMpSgv/aPx+AgMKC9KjGlp5iwRC5GIsqV8C9sQ5Sv3EwDz8PUt/Q/XHf7CxG7+wk9M8P38NM1w24YYKYnNmhARoQCNIanAqyvJnAmK6T2QfJVzwG5dBGaMV7oRz4DsqeL2C78E7IfUfHa7gRcylaSLXPljBIIyIiIqIeySSLaHSqcbmWbngCix/PGoLjpfXISg0EFm7FEwiG+3LuKwJS16jgva8PY/O+Mn+QJIgSzKPnQB44CZVHTwLvlSBFcGDJ2OjaX4mCgLEpVdB1ARWTbsaAM0KXJx4oqsHvX9rsf/6nW85BZmrzwMi39FBMwIpBX2GNdVtP4uoLCtt1LcGaDPPImcDImTDcDiiHNkDqMxIAoNeWQUjJTtgySLdbQ3Jq60VDAAZpRERERNRDmeU4LnfUdQiigAvO6tfsPVX3BGmy1DwQMHuXLm7ZX47PNh0HABwtDm30LCZloP/IDMwrtmBsw9fI++YNNOwfAMuUKyH3ajmAMZz10GtLIeUOwon8GVi5ewjcnzbihTNCjwsO0ABg874yzJnUvCm0b3uYlIAobezgrA65rmC2wTx8BgBAb6hCw9u/hdx3NKwzb4QgtR08tZfT3XqFTZ+OzVMSEREREXVRJlmKW+EQXW85ePE1tZbDVGX0LXesqne1+RmXzxqCwjlLYDlnKQxnHRzv/wHOb16B3lAVcpxhGFCPbUPD2w/A8elfYKhuWMwS3Gg9CPl/V08EALjc4QNXXyYtEVknURCQmmRudwPu1gi2NJjHL4B6aCOca5+D0YGfBQCHT9WitNoRNlhvipk0IiIiIuqRLCYR7jg1s9YNA2ILwYvmregXLojz7V37fm9ZRJ8jWJNhHj0HpsJpcH3zCpQdn8BwNcB23vUwnPVQDnwL5cB66KWHIKTmwTb75xBksz9jF86QPmk4VlKHQb097QHWbT2JhecMCDmmvMaBshpHi/fREUQhtuqOkRJEEZbxCwEA7g1vwp3VD5bxCzrs80oqGwEAE4e1vVyVQRoRERER9UgmWYJL1VsNsCKl66HVHYP5lreFW+aWk25DdpoV5TXOqD5PMFlhnX4dzOMuBLxj12tL4Vr/EsT03rCc+xOYhp8HQfJ83c/LtLd4Ladbw8gBmf4MWUVt87Hc9fQ3/sct3We8iaLQoZk0H/O4BdDLj3lK9fcqhJQ3pEM+x1d+v29OUpvHMkgjIiIioh7JbBLhcmu4/tE1eOGeWe26VmuB3lWzh6EgJxkjB2Q2e08QBPTNSUZ5jRNmWcTdS8+A3Rr5V/TgBtJiZl8kXfEYhJScZksSZ53RB//+bB8Az3LI4PcdLgV2a3LI8R9/dwzzzu4HwzDw7Pu7Qj8zYZk0AXp8VqO2ShAEWGcshzuzD8TsAR32Of5lrxH0ueOeNCIiIiLqkUxRNoVuja+ZdTjJNhPmT+7fYhCXl+npYza0IB0De6UiL6PlrFdrBNkMMTU37J4xQQiU7leb9B5rdKnNAsNvdxUDAOocCr7bVeJ/feKwnHZnHSMlCkKH7xPzEUxWWM5YDEGSYTjrYWhK3D/jX5/sBRB+b2JTDNKIiIiIqEdKsQUKaaha+1I2um7EXFBj8dSBuOy8wbhiVscss/MZ462YGLyEUNcNOFwa7BZPkPaTucOQkWJBdb2nt1txRWPINa67cESHjjGYkKDljsEMtwMNKx6E69vX4nrd4PuI5I8DDNKIiIiIqEeaOrY3Rg/yLEFsaEe/tLJqB3YdqYq5oIbVLOPCyf3RJye57YPbwZcBCw5I9xdVAwCSrJ6AddYZfXHm8Fy43BoMw8C6rSf9x04d0yuqpZjtVVLZiA27SxP2eYCnRL/cfzyUH1ZBPbo1btcNrphpYiaNiIiIiCg8kyxi5vg+AIDKMMUy2rJu6wksf2Q1Hv7XZrgUDZV10V8jkXxLBx/8+0YAgEvR8OjLWwAgJPiymCS4FQ0HTtTgmx+K/a9nploSONqARGfTLGdfDjGrAM51zzdrbxArZ1CQFsmePgZpRERERNRjZaZaAcQWpH2ywdN8urbBszSwsR3ZuERwedsNlNc4YRgGnK7AeEOCNLMEA4H7A4BbLx3TrCx/omhaYoM0QTLBev7PYKguONc8CyMO1Uscruh+NxikEREREVGP5WsmHW1Ta4dLRVaTzFIimjzHS6NL9d+zJAoY5i0qAgSW432/z9O77dr5wzFhWE5EBS86Qnv3C8ZCSu8NyzlLPU8UR7uv57uHn18yJqLjWYKfiIiIiHosX0CiRBkIPPj3jSitDv3ynqDK9DELXjXocKr+e/7pwhH+PWme40IzV2MGZSVkfC3ROrChdWtMhdNhKpwGQWh/cOovvy9F9kvCTBoRERER9Vi+nlVNy9K3pWmABgBL5w6Ly5g6SvAdOtyaP5PWtJBFit0c8txi6tyQQeuETBrgyYwKggi9sRqOtf8Hw9UQ87U075LJSLORDNKIiIiIqMfyfWlWo1ju6NvbFeyCswowbWzvuI2rYwTCNIdL9QemTUvCnzkiF+dN6ON/bpKlxAyvBdEG0OE8/8EuLH9kdUznGvUVUPd/C+cXf4+5bxszaUREREREEfJlkaLZ9/TAs980ey0tqXMqH0YjOL5wulUoqifYbJpJkyURP5kTyArGs+l3LLQ4FO74ekdx2we1QModDMtZl0E9vAnKrtgCPd89SCIzaURERERErZJlT2Zjf1FNxMHAD4cqAABnjcjFpMIcAAgpvHE6cLg0uBTvcscwmbJIysQnSjwyae1lGnsBpIKxcH3zCrTyo1Gf77sHiZk0IiIiIqLW+TIbWw+U4821B6M6Ny/DjuULRuDXl4/DoN6pHTG8uArOpNXUu/DEG9sAALkZthbPGTu4c4uGAPEtHBJrzzVBEGGdeQMEazJcG96I+nzNv9wxsvCL1R2JiIiIiODJpkWj3qnAapYxupOrH0bKCNqT9urqAwA0z0VJAAAaRUlEQVSA4f3SkZpkDnv8M3eehwhX53WoeJbgV1UdZlNse+xEawps826HmJIT9bmB5Y7MpBERERERtcmXLQpXECScSSPyAADzzurXYWPqEGGSSBdPG9Ti4SZZjHgPVUe45eLRAABdN1DvUHDjH9dgz9Gqdl2zvQGflD0AgiUJhq5Cr6+M+Lzd3nGzcAgRERERUQR8X5udLjXic/rnpSAnveVlgl1RuIV+LWXRugKbxbPoTzcMHDpZC1Uz8OG30e8HC6bEaX+b45P/geOTx2Hokf3O7D7iCdKatjdoCYM0IiIiIiIAjREGaYqqdXrFw1gM7p0GALCYA8v9UiMMGjqDb2WgrhsQvI+r6l3Q27FHzVfRsr1MI86DXnEc7m0ftXmsYRioaXBj9sS+/sCzLaffbxcRERERURwJ3gjA4Wr7C3yDU8G2/eURL1vrSiYW5uDPPz8Xyy8c4X/NZuncHmit8VWY1HTDn+08UdaATzcej+o6wb3N4lUp0jRgIuSBk+D+/l3o1adaPbbRpcKlaMhKs0Z8fQZpREREREQRKiqtBwD0y0vp5JHEJiPFgjOH5+LhG87Gf153pj9A7Yp8++H+9OpWBCfPjpfWRXWdE2UN/sfRNC1vi+XcnwCyBY51z8NopX3DDm/LhoyUyHvpMUgjIiIiIopQVb0LADB1TK9OHkn79MpK6vKBphAUqbiDirpYI1wy6FPX6PY/VuJYKVK0p8M65SpAccJwtFwZtKrW8zszpE9axNdmCX4iIiIiIq+jxXXon99y8HKwqBYAkJkaeVaEYhNcrl4NylSZo9wP6HQHArx4lvMHAHnoOZCHTIYgNl82+t+vbcXEwhx/1dB0ZtKIiIiIiCIzfmi2//GuI5XYcagCu46EL6/e4FSQl2mH3WpK1PB6LDFoKaaiBIIrkxzdPrqQIC2Oyx0Bz35GQZRguBrg3rMu5L2dhyvxj4/3wunWYJbFkPtpC4M0IiIiIurRpo3thad+NR0mWUSdQ8Hjr2/Dn17dGvZYRdVDqiNSxxGDMmnuoOBKjrAhtI/THajaGa8S/E0pe76A64u/Qz22HUCgeTUAHDxZE3UDbQZpRERERNSjCYIAm0WGour4+Ltj/tf3HmveOFnR9KiX21Fsgpc7KkFBmm5EF2gFZ9Lcihb1+ZEwjZ4DMb03nF//E4bqgjso81dd54aFQRoRERERUfs9+vKWZq8pqh71cjuKTXDlyeD+ZlqUfdIcQUHaX9/Zif95Y3v7B9eEIMmwTFsGo64c7u/fCwkqK2qdSE+Orh8dgzQiIiIiolaUVDZi894yAN5MmolfoRMhuL9ZcFXGaJtZBy93BAIl8eNN7lUIedg0uLd9DKX8WMh7uRn2qK7F3zAiIiIiIgB9spPCvn7/89/hqRU7AADl1Q5m0hIkOBYLzkzVNLixdsuJkCCuJXuOVqGuUemI4YVlmXw5pEGT8PLa0Ibb184fHtV1GKQREREREQH47bWTwr6ueotNHC2uQ3W9G7LUdRtAdyvBmbSgIG39zmL885O9KApqUh1OaVUjHntlC77bVYJkW2g1TldQ37V4Eq0pKBm5FFtOhQaQpij3MTJIIyIiIiKCp7T7zDP6tPj+a6v3AwDmnzMwUUPq0TJTrf7HviAtONipqnO2en6jK7DMMcUeGqQdL6kPea5qetx6qDldKjLEevx+xHYkC46YrsEgjYiIiIjIa8a43v7Hw/ulh7y351g1rGYJZxTmJnpYPZLNIuNnF48GEKjQaJIC4ctf3tzR6vnBqyGbZtJWfnMk5Pmvn/wadzz1dURLKNviVnWYoMFeugMX2zfHdA0GaUREREREXv3yUvDXX08H4AnKSqtDMyHB5dyp4/myWxv3lAIATEFFW3TDQKMzsv1mSUHNx/tkJ6G63hXyfr1DQV2jgp8+ugbFlY0RXbOqzoW/vbsTria/E4qqo1RPg2voHJxpOYRrxkWfoWOQRkREREQUxGqW/Y9PlNW3ciR1NDVoL5qA0J8NAJRVt7zkMXj5otUSKPYydkgWTpQ1wOlW8dSKHfiP574LOW/HwciqP6744hA27C7Fht0lIa/7lmZqo+ZBSM3DpOqPYTij+z1ikEZERERE1MRP5g4DALy97lDI63PPLOiM4fRYwY2nJUlAmndvmW8pall1y3u+gouN2Cyy/7zcdBs03UCjU8XmvWU4Wd4AOWgZ5c7Dlc2yY+HI3v1xVU2ycr52AWaLFbZZN8ForIZ724f+97WKY3CseabVazNIIyIiIiJqYmhfTxBwojy0gmBhQXq4w6mD+CprAoAkikhN8jSFTku2AAD+b+UuVNSEz6YFB2l2i4wnfjkVv7p8nL/4yIbdpUGfEzh2x6EKPPfBrjbHZvcGfp9tDJTb/+FIJV78aA8AT5ETKXcQrOf/DKYxFwAA3D98jsYVD8KoPtXqteVW3yUiIiIi6oEsLTSsjraUOrWPpgcHaQJSvEFaktUTxiiqjs82HcenG48jySrjf2+f7j++aSYt1e4515c1e33NgZDPmj2xL1ZtLgIAbI9gyaPvdyR4v9sn3wWaWPuKlZgGTPS/plcch6lwOqyTf9TqtflbRkRERETUhD3oi/eZw3ORm2EDwCAt0bSgDJckCUjxBj7BP59t3oCqwamGnKtozZc7Ai3/DAf3ScO8s/p5zlV1OFwqHnpxI77dVRz2eJfiub6BQCDpDOq/JgjN++lZp18L67RlEC3hG6f78LeMiIiIiKiJZJvJv7QxySojPSk0C0OJETzfkihg2tjeWDClP6aP6+V/XWuhv1loJi1QOEQSA9cM3mPYJycJsyf1BQCMHZyFgydrcKS4Ds++twtb95djy/4yVNUF9p/5lkgGL8l0ujxB2m2XjY3uRpvgbxkRERERURhnj8oD4GmKfPmsoZgyKh99c5I7eVQ9y3kTAs3FJVFEVpoVS2YMRnaazf+63kJvs6Z70nxcQdku395DAOibk4zMVCvyM+2wmCTUNQTK+//1nR3437d24I6nvsbmvZ69bJo3OAv+HKdbxZRR+Rg3JDvqew3GPWlERERERGGk2DzZM6dbw6DeqRjUe2Qnj6jnMckibBYZDpeKitrwBUIqawPZLYdLhc0iwzAMfy80URCQnxVYXhjcsDo7zYrJo/Iwa0Jf/2tWswSnW4PTHVg+GZwt8/XO03RfJi04SNNCyv3Hipk0IiIiIqIwMlM9FQSdLrWNI6kj5WfaIz5264FyAMC6bSfxwTdHAQB/u3MGctMDmbdJhbn+xyl2E25cNApD+qb5X/MEaaq/cXmad6mrzxtrDqKorN4fuPn+1zAM1DsU2Mztz4MxSCMiIiIiCqN/fgrOP6Mvrpw9rLOH0qP96vJxER/r8AbUG3YFGkxLYmgBD1EU8MjNU3DXlROQmWptdg2rWcb+ohq8sfYgAGDckKxmx/z2+Q2od3iWQ6qap8jITx9d4z2fmTQiIiIiog4hCgKWzh2G/vkpnT2UHs1Xyj4Su49WobTagfQUTxb07qsmhK2ymJtuw/D+GWGvEbxcUYCn6mM4NQ2BZZaHT9X6H8ejAij3pBERERERUbeweW8Z9hyt8pfjL+wXPhBrjTVoueIfbzkHRWWehuYmWQwpEtLgCCyD3X20yv9YDBMURotBGhERERERnXamjMqH062id3YS0pMt+Pdn+wA075cWLd9yRUEAMlIsSE+24Nr5wzFlVB5u+tM6/3G+AiIA/PvfAMASh+WODNKIiIiIiOi0c8Oi0GqbviDN59wx+TFd1xekmU0SBEGAIADTx/UGANy6ZAy2H6zAuq0nWzx/ZAvLKKPBII2IiIiIiLq0c0bnt5mhEgAEd0zrHVR2Pxo2b0+1YUE91HwmDM3BuMHZYYO0BVP6Y8mMwTF9ZlMM0oiIiIiIqEu7fmHbPeqatrQWxdj2hp01Ig8Op4rJ3mbmTYmigBS7CXWNCpbOGQaTLCIjxYIxg5pXgYwVqzsSEREREdFp7+eXjA6pBNm09H6k0pLMuGjqQORmtNyfbYi34mNakhnTx/WOa4AGMJNGRERERETdwMTCXAzqnYY7nvoaQOxBWiSunT8c08bWYvSgzA65PoM0IiIiIiLqFkIyaVLHLRpMsZsxfmh2h12fyx2JiIiIiKhbCG4k3ZGZtI7GII2IiIiIiLqdWAuHdAUM0oiIiIiIqNuwmDyl+plJIyIiIiIi6gLsVk/ZDQZpREREREREXcCgXqkAAk2pT0en78iJiIiIiIiauO7C4ZhzZgGG9E3r7KHEjEEaERERERF1G3arCcMK0jt7GO3C5Y5ERERERERdCIM0IiIiIiKiLqRdyx2vvvpqVFZWQpY9l3nooYcwbty4uAyMiIiIiIioJ4o5SDMMA0eOHMGaNWv8QRoRERERERG1T8zLHQ8dOgQAWL58OS666CK89NJLcRsUERERERFRTyUYhmHEcuKWLVvwyiuv4P7774eiKLjmmmtw77334txzz433GImIiIiIiHqMmIO0pl588UWcPHkS9913X0THV1TUQ9fj8tHdXk5OCsrK6jp7GKcVzln0OGfR45xFj3MWHc5X9Dhn0eOcRY9zFj3OWShRFJCVldzy+7FeeNOmTfjmm2/8zw3D4N40IiIiIiKidoo5SKurq8Njjz0Gl8uF+vp6rFixAnPmzInn2IiIiIiIiHqcmFNfM2fOxLZt23DxxRdD13VcddVVmDBhQjzHRkRERERE1OO0a33i7bffjttvvz1eYyEiIiIiIurxYl7uSERERERERPHHII2IiIiIiKgLYZBGRERERETUhTBIIyIiIiIi6kIYpBEREREREXUhndZ9WhSFzvro0xLnK3qcs+hxzqLHOYse5yw6nK/occ6ixzmLHucsepyzgLbmQjAMw0jQWIiIiIiIiKgNXO5IRERERETUhTBIIyIiIiIi6kIYpBEREREREXUhDNKIiIiIiIi6EAZpREREREREXQiDNCIiIiIioi6EQRoREREREVEXwiCNiIiIiIioC2GQRkRERERE1IUwSCMiIiIiIupCGKR1oieffBILFizAggUL8NhjjwEA1q9fj0WLFmHu3Ll4/PHHm51z11134e233w557Y033sA999yTkDF3pnjM1+bNm3HZZZdh8eLFWLZsGU6cOJGw8XeGeMzZpk2bcOmll2LRokW4+eabUVNTk7Dxd4Z4/f8SAHbt2oXRo0d3+Jg7WzzmbMWKFZg6dSoWL16MxYsXhz2nu4jHfJWWluLGG2/ExRdfjCuuuAJFRUUJG39naO+cVVRU+H+3Fi9ejFmzZmHChAkJvYdEi8fvWVFREZYuXYrFixfj6quv5n8zI5iz7du3Y8mSJVi0aBFuuukmlJWVJWz8nSGaOVu1ahUWL16Miy66CLfccov/+8TJkyexdOlSzJs3Dz/72c/Q0NDQKffS5RjUKb7++mvjxz/+seFyuQy3221cc801xvvvv2/MmDHDOHbsmKEoirF8+XJj7dq1hmEYRnFxsXHTTTcZY8eONd566y3DMAzD6XQaf/zjH43x48cbd999d2feToeLx3wZhmHMnDnT2L17t2EYhvHGG28YN998c6fcTyLEa85mz55t7N+/3zAMw/jjH/9o/PnPf+6U+0mEeM2ZYRhGY2OjccUVVxjDhg3rjFtJmHjN2UMPPWS8//77nXUbCROv+Vq2bJnx8ssvG4ZhGC+//LJx2223dcr9JEI8/39pGIahaZrxk5/8xHjvvfcSfSsJE685u/POO41///vfhmEYxj//+U/jjjvu6JT7SYR4zJmu68aMGTOMb775xjAMw/jggw+Mm266qdPuqaNFM2d1dXXGueeeaxQXFxuGYRhPPPGE8bvf/c4wDMO48cYbjZUrVxqGYRhPPvmk8dhjj3XaPXUlzKR1kpycHNxzzz0wm80wmUwYPHgwjhw5gv79+6OgoACyLGPRokX4+OOPAQDvv/8+zj//fMyfP99/jY0bN0LXdfzmN7/prNtImHjMl9vtxm233Ybhw4cDAAoLC3Hq1KlOuZ9EiMecAcCHH36IIUOGQFEUlJSUIDU1tTNuJyHiNWcA8Mgjj2DZsmWJvoWEi9ec7dixAytWrMCiRYtw5513dtuMbTzmq7KyEnv27MEVV1wBAFiyZAluv/32TrmfRIjn/y8B4K233oLNZsOiRYsSeRsJFa8503Ud9fX1AACHwwGr1Zrwe0mUeMxZVVUVnE4nJk+eDACYOXMmvvrqK7jd7k65p44WzZwpioIHHngAeXl5AALfwRRFwcaNG3HBBRcAAC699FL/HPd0DNI6ydChQzF+/HgAwJEjR/DRRx9BEATk5OT4j8nNzUVJSQkA4Prrr8ePfvSjkGtMnToVd911V7f+R9MnHvNlNpuxePFiAJ7/8Dz55JOYPXt2gu4g8eIxZwBgMpmwd+9ezJgxA9999x0WLFiQmBvoBPGas88//xxOpxPz5s1LzMA7UbzmLCcnB7fccgvee+899OrVCw899FBibiDB4jFfx48fR+/evfHII49gyZIl+OUvfwmTyZS4m0iweP2OAYCmafjb3/6GO+64o+MH3oniNWe33XYbXnzxRUybNg0vvPACbrjhhsTcQCeIx5xlZGTAbrfjq6++AgB88MEHUBQFVVVVCbqLxIpmzjIyMjBnzhwAgNPpxLPPPovZs2ejqqoKycnJkGUZgOe/Bb457ukYpHWy/fv3Y/ny5bjrrrtQUFAAQRD87xmGEfKc4jNfbrcbd955J1RVxU033dSRw+0S4jFnhYWFWL9+PW655Rb86le/6sjhdgntmbOysjI8/fTTuP/++xMx1C6jvb9nTz31FCZOnAhBEHD99dfjyy+/7Oghd6r2zJeqqti1axcmT56Mt956C+eff36P2Jccj3/LvvzySwwYMACFhYUdOdQuo71zdvfdd+Ohhx7Cl19+iQcffBC/+MUvYBhGRw+7U7VnzgRBwF/+8hc888wzuPjii1FXV4f09PRu/UcUILo5q6urw4033ojhw4fjkksuCTun/O7rwSCtE23evBnXXnst7rjjDlxyySXIz88P2WBaVlaG3NzcThxh1xKP+WpoaMD1118PVVXx9NNPd/t/ONs7Zy6XC6tWrfI/v+iii7B3794OHXNna++crV27FtXV1f7N9gCwePFi/5Kh7qi9c1ZXV4cXX3zR/9wwDEiS1JFD7lTtna+cnBwkJSVh5syZAICFCxdi+/btHT7uzhSv/16uWrUKF154YUcOtcto75xVVlbi0KFD/hUnF1xwAcrKyrptVgiIz++ZLMv417/+hXfeeQcXXXQRdF1Henp6Rw+900QzZ6WlpbjqqqtQWFiIhx9+GACQmZmJuro6aJrW7PiejkFaJzl16hR+/vOf409/+pN/+di4ceNw+PBhHD16FJqmYeXKlZg+fXonj7RriNd8/eY3v0H//v3xxBNPwGw2J2LonSYecybLMh588EHs3LkTAPDRRx/hjDPOSMj4O0M85uxHP/oRVq1ahXfffRfvvvsuAODdd99FcnJyQu4h0eIxZ3a7Hc899xy2bdsGAHjppZf8y2K6m3jMV79+/ZD//9u7f9Cm1jgO4096rrSYLFXQiotDCbF2rJuQIhTtVLsJgoiTWBRaUEr9k2IRqpZYaFFoKU4uRUQcBEWxggqig1Lp4KJoRdBRqon1pHfwEhDvxSvE5HjyfMZwEn7vy3vI+Z73PedtaeHevXsA3L17ly1btlSl/lqo5P/l06dP6ejo+N0l11wl+qy5uZnGxkaePHkCfLsYTyaTrFmzpiptqLZKjbOhoaHyTZNLly6xc+dOGhriebn9K30WhiEHDhygu7ubY8eOlWfLVq1aRUdHBzdu3ADg2rVrXvv+469aF1CvZmZmKBaLjI6Olj/bvXs3o6OjHDp0iGKxSDabrYtnWv6PSvTXwsICd+7cobW1ld7eXuDbWunp6enfXn8tVKLPgiDg/PnznDx5kjAMWb9+ffnuVxx5Xv66So2z8fFxhoeHKRQKbNq0qfwq57ip1BibmJggl8tx7tw5UqnUd78XN5U8L9+8eUNLS8vvLDcSKtFniUSCyclJRkZGKBQKJJNJJiYmqlF+TVRqnA0PD5PL5fj8+fN3M0Zx9Ct9dvv2bRYWFgjDkJs3bwLQ3t7O6dOnyeVyDA4OcvHiRTZs2EA+n69VkyIlsRL3xcWSJEmS9AeJ5/yrJEmSJP2hDGmSJEmSFCGGNEmSJEmKEEOaJEmSJEWIIU2SJEmSIsRX8EuSYmFxcZGuri7S6TQApVKJZDLJ3r17f7qB8eTkJJlMprxxryRJtWRIkyTFRlNTU3kTcYC3b9+yb98+giBgx44d//m9R48e0draWo0SJUn6KUOaJCm2Nm7cyOHDh5mZmSGdTnPq1CmWlpb48OEDmUyG8fFxrly5wvPnzzl79ixBEJDNZhkbG+Px48eEYUhbWxvHjx8nlUrVujmSpDrhM2mSpFjLZDK8ePGC2dlZdu3axezsLLdu3WJxcZG5uTn27NlDe3s7R48epauri6mpKYIg4OrVq1y/fp1169YxNjZW62ZIkuqIM2mSpFhLJBI0NTVx5MgRHjx4wPT0NK9eveL9+/d8+vTph+Pn5ub4+PEjDx8+BGB5eZm1a9dWu2xJUh0zpEmSYm1+fp50Os3AwABhGNLd3U1nZyfv3r1jZWXlh+NLpRJDQ0Nks1kAlpaWKBaL1S5bklTHXO4oSYqtly9fcuHCBfbv38/9+/fp6+srv+nx2bNnhGEIQBAEfP36FYBt27Zx+fJlvnz5QqlU4sSJE+Tz+Zq1QZJUf5xJkyTFRqFQoKenB4CGhgYaGxsZGBigs7OT/v5++vr6WL16NalUiq1bt/L69WsAtm/fTj6fZ3l5mYMHD3LmzBl6e3sJw5DNmzczODhYy2ZJkupMYuXf1npIkiRJkmrC5Y6SJEmSFCGGNEmSJEmKEEOaJEmSJEWIIU2SJEmSIsSQJkmSJEkRYkiTJEmSpAgxpEmSJElShPwNun1OHY/aZkgAAAAASUVORK5CYII=\n", "text/plain": ["<Figure size 1080x576 with 1 Axes>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(15,8))\n", "\n", "# plot data\n", "ge.plot()\n", "\n", "# plot 250 day rolling mean\n", "ge.rolling(250).mean().plot(style='--');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The plot shows GE stock price data. The __dashed line__ represents 250-day moving window average of the stock price."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Time Zones\n", "We live in a global world where many companies operate in different time zones. This makes it crucial to carefully analyze the data based on the correct time zone. Many users work with time series in __UTC__ (coordinated universal time) time which is the current international standard. Time zones are expressed as offsets from UTC; for example, California is seven hours behind UTC during daylight saving time (DST) and eight hours behind the rest of the year."]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Localization and Conversion"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Time series objects in Pandas do not have an assigned time zone by default. Let's consider the GE stock price `ge` data as an example."]}, {"cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04    14.855769\n", "2010-01-05    14.932693\n", "2010-01-06    14.855769\n", "2010-01-07    15.625000\n", "2010-01-08    15.961538\n", "Freq: D, Name: Close, dtype: float64"]}, "execution_count": 71, "metadata": {}, "output_type": "execute_result"}], "source": ["# print data\n", "ge.head()"]}, {"cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["None\n"]}], "source": ["# check timezone\n", "print(ge.index.tz)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The index's `tz` field is `None`. We can assign a time zone using `tz_localize` method."]}, {"cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2010-01-04 00:00:00+00:00', '2010-01-05 00:00:00+00:00',\n", "               '2010-01-06 00:00:00+00:00', '2010-01-07 00:00:00+00:00',\n", "               '2010-01-08 00:00:00+00:00', '2010-01-09 00:00:00+00:00',\n", "               '2010-01-10 00:00:00+00:00', '2010-01-11 00:00:00+00:00',\n", "               '2010-01-12 00:00:00+00:00', '2010-01-13 00:00:00+00:00',\n", "               ...\n", "               '2020-11-16 00:00:00+00:00', '2020-11-17 00:00:00+00:00',\n", "               '2020-11-18 00:00:00+00:00', '2020-11-19 00:00:00+00:00',\n", "               '2020-11-20 00:00:00+00:00', '2020-11-21 00:00:00+00:00',\n", "               '2020-11-22 00:00:00+00:00', '2020-11-23 00:00:00+00:00',\n", "               '2020-11-24 00:00:00+00:00', '2020-11-25 00:00:00+00:00'],\n", "              dtype='datetime64[ns, UTC]', name='Date', length=3979, freq='D')"]}, "execution_count": 73, "metadata": {}, "output_type": "execute_result"}], "source": ["# localize timezone\n", "ge_utc = ge.tz_localize('UTC')\n", "ge_utc.index"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Once a time series has been localized to a particular time zone, it can be easily converted to another time zone with `tz_convert`."]}, {"cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2010-01-03 19:00:00-05:00', '2010-01-04 19:00:00-05:00',\n", "               '2010-01-05 19:00:00-05:00', '2010-01-06 19:00:00-05:00',\n", "               '2010-01-07 19:00:00-05:00', '2010-01-08 19:00:00-05:00',\n", "               '2010-01-09 19:00:00-05:00', '2010-01-10 19:00:00-05:00',\n", "               '2010-01-11 19:00:00-05:00', '2010-01-12 19:00:00-05:00',\n", "               ...\n", "               '2020-11-15 19:00:00-05:00', '2020-11-16 19:00:00-05:00',\n", "               '2020-11-17 19:00:00-05:00', '2020-11-18 19:00:00-05:00',\n", "               '2020-11-19 19:00:00-05:00', '2020-11-20 19:00:00-05:00',\n", "               '2020-11-21 19:00:00-05:00', '2020-11-22 19:00:00-05:00',\n", "               '2020-11-23 19:00:00-05:00', '2020-11-24 19:00:00-05:00'],\n", "              dtype='datetime64[ns, America/New_York]', name='Date', length=3979, freq='D')"]}, "execution_count": 74, "metadata": {}, "output_type": "execute_result"}], "source": ["ge_ny = ge_utc.tz_convert('America/New_York')\n", "ge_ny.index"]}, {"cell_type": "markdown", "metadata": {}, "source": ["__Epoch time__ can be read as timezone-naive timestamps and then localized to the appropriate timezone using the `tz_localize` method."]}, {"cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [{"data": {"text/plain": ["Timestamp('2010-01-01 12:00:00-0800', tz='US/Pacific')"]}, "execution_count": 75, "metadata": {}, "output_type": "execute_result"}], "source": ["# Localize epoch as a timestamp with US/Pacific timezone\n", "pd.Timestamp(1262347200000000000).tz_localize('US/Pacific')"]}, {"cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2010-01-01 12:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)"]}, "execution_count": 76, "metadata": {}, "output_type": "execute_result"}], "source": ["# Localize epoch as a DatetimeIndex with UTC timezone\n", "pd.DatetimeIndex([1262347200000000000]).tz_localize('UTC')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Operating between TIme Zones\n", "\n", "If two time series with different time zones are combined, the result will be UTC."]}, {"cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Date\n", "2010-01-03 19:00:00-05:00    14.855769\n", "2010-01-04 19:00:00-05:00    14.932693\n", "2010-01-05 19:00:00-05:00    14.855769\n", "2010-01-06 19:00:00-05:00    15.625000\n", "2010-01-07 19:00:00-05:00    15.961538\n", "2010-01-08 19:00:00-05:00    15.961538\n", "2010-01-09 19:00:00-05:00    15.961538\n", "Freq: D, Name: Close, dtype: float64\n", "Timezone of ts1:  America/New_York\n", "\n", "Date\n", "2010-01-03 16:00:00-08:00    14.855769\n", "2010-01-04 16:00:00-08:00    14.932693\n", "2010-01-05 16:00:00-08:00    14.855769\n", "2010-01-06 16:00:00-08:00    15.625000\n", "2010-01-07 16:00:00-08:00    15.961538\n", "2010-01-08 16:00:00-08:00    15.961538\n", "2010-01-09 16:00:00-08:00    15.961538\n", "Freq: D, Name: Close, dtype: float64\n", "Timezone of ts2:  America/Los_Angeles\n"]}], "source": ["# create two time series with different time zones\n", "ge_la = ge_utc.tz_convert('America/Los_Angeles')\n", "\n", "ts1 = ge_ny[:7]\n", "print(ts1)\n", "print('Timezone of ts1: ', ts1.index.tz)\n", "print()\n", "\n", "ts2 = ge_la[:7]\n", "print(ts2)\n", "print('Timezone of ts2: ', ts2.index.tz)"]}, {"cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Date\n", "2010-01-04 00:00:00+00:00    29.711538\n", "2010-01-05 00:00:00+00:00    29.865385\n", "2010-01-06 00:00:00+00:00    29.711538\n", "2010-01-07 00:00:00+00:00    31.250000\n", "2010-01-08 00:00:00+00:00    31.923077\n", "2010-01-09 00:00:00+00:00    31.923077\n", "2010-01-10 00:00:00+00:00    31.923077\n", "Freq: D, Name: Close, dtype: float64\n", "Timezone of ts3:  UTC\n"]}], "source": ["# add two time series\n", "ts3 = ts1 + ts2\n", "print(ts3)\n", "print('Timezone of ts3: ', ts3.index.tz)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["#### Common Time Zones\n", "Time zone information in python comes from a third party library called `pytz` (installable using `conda install pytz`). Let's look at some examples."]}, {"cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": ["# import library\n", "import pytz"]}, {"cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [{"data": {"text/plain": ["['Pacific/Wake',\n", " 'Pacific/Wallis',\n", " 'US/Alaska',\n", " 'US/Arizona',\n", " 'US/Central',\n", " 'US/Eastern',\n", " 'US/Hawaii',\n", " 'US/Mountain',\n", " 'US/Pacific',\n", " 'UTC']"]}, "execution_count": 80, "metadata": {}, "output_type": "execute_result"}], "source": ["# common time zones\n", "pytz.common_timezones[-10:]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["To get a time zone object, `pytz.timezone` can be used."]}, {"cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [{"data": {"text/plain": ["<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>"]}, "execution_count": 81, "metadata": {}, "output_type": "execute_result"}], "source": ["tz = pytz.timezone('US/Pacific')\n", "tz"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Common Use Cases"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Importing data is the first step in any data science project. Often, you\u2019ll work with data that contains date and time elements. In this section, we will see how to: \n", "1. Read date columns from data.\n", "2. Split a column with date and time into separate columns.\n", "3. Combine different date and time columns to form a datetime column."]}, {"attachments": {}, "cell_type": "markdown", "metadata": {}, "source": ["We will use sample earthquake data with date and time information to illustrate this example. The data is stored in '.csv' format as an item. We will download the '.csv' file in a folder, as shown below, and then import the data for analysis.\n", "\n", "Note: the dataset used in this example has been curated for illustration purposes."]}, {"cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [{"data": {"text/html": ["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n", "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n", "                       <a href='https://www.arcgis.com/home/item.html?id=008fcafa23a24351b4f37f7c8a542cb1' target='_blank'>\n", "                        <img src='https://www.arcgis.com/sharing/rest//content/items/008fcafa23a24351b4f37f7c8a542cb1/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n", "                       </a>\n", "                    </div>\n", "\n", "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n", "                        <a href='https://www.arcgis.com/home/item.html?id=008fcafa23a24351b4f37f7c8a542cb1' target='_blank'><b>earthquakes_sample_data</b>\n", "                        </a>\n", "                        <br/><img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/layers16.png' style=\"vertical-align:middle;\">CSV by api_data_owner\n", "                        <br/>Last Modified: November 19, 2020\n", "                        <br/>0 comments, 7 views\n", "                    </div>\n", "                </div>\n", "                "], "text/plain": ["<Item title:\"earthquakes_sample_data\" type:CSV owner:api_data_owner>"]}, "execution_count": 82, "metadata": {}, "output_type": "execute_result"}], "source": ["# Get item with data\n", "import os\n", "from arcgis import GIS\n", "\n", "gis = GIS()\n", "earthquake_item = gis.content.get('008fcafa23a24351b4f37f7c8a542cb1')\n", "earthquake_item"]}, {"cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Created data folder at: ../../samples_data\n", "earthquakes_data.csv downloaded\n"]}], "source": ["# Download data item\n", "\n", "# Create folder for file download\n", "data_folder = '../../samples_data'\n", "if not os.path.exists(data_folder):\n", "    os.makedirs(data_folder)\n", "    print(f'Created data folder at: {data_folder}')\n", "else:\n", "    print(f'Using existing data folder at: {data_folder}')\n", "    \n", "# Download file\n", "filename = 'earthquakes_data.csv'\n", "if not os.path.exists(os.path.join(data_folder, filename)):\n", "    earthquake_item.download(data_folder, 'earthquakes_data.csv')\n", "    print(f'{filename} downloaded')\n", "else:\n", "    print(f'{filename} exists')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Import data with date/time\n", "Data with dates can be easily imported as `datetime` by setting the `parse_dates` parameter. Let's import the data and check the data types."]}, {"cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>datetime</th>\n", "      <th>latitude</th>\n", "      <th>longitude</th>\n", "      <th>depth</th>\n", "      <th>magnitude</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1973-08-09 02:18:00</td>\n", "      <td>40.260</td>\n", "      <td>-124.233</td>\n", "      <td>2.0</td>\n", "      <td>5.1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1976-11-27 02:49:00</td>\n", "      <td>40.998</td>\n", "      <td>-120.447</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1977-02-22 06:24:00</td>\n", "      <td>38.478</td>\n", "      <td>-119.287</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1978-09-04 21:54:00</td>\n", "      <td>38.814</td>\n", "      <td>-119.811</td>\n", "      <td>14.0</td>\n", "      <td>5.2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1979-10-07 20:54:00</td>\n", "      <td>38.224</td>\n", "      <td>-119.348</td>\n", "      <td>11.0</td>\n", "      <td>5.2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             datetime  latitude  longitude  depth  magnitude\n", "0 1973-08-09 02:18:00    40.260   -124.233    2.0        5.1\n", "1 1976-11-27 02:49:00    40.998   -120.447    5.0        5.0\n", "2 1977-02-22 06:24:00    38.478   -119.287    5.0        5.0\n", "3 1978-09-04 21:54:00    38.814   -119.811   14.0        5.2\n", "4 1979-10-07 20:54:00    38.224   -119.348   11.0        5.2"]}, "execution_count": 84, "metadata": {}, "output_type": "execute_result"}], "source": ["# Read data\n", "quake_data = pd.read_csv('./samples_data/earthquakes_data.csv', parse_dates=['datetime'])\n", "quake_data.head()"]}, {"cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime     datetime64[ns]\n", "latitude            float64\n", "longitude           float64\n", "depth               float64\n", "magnitude           float64\n", "dtype: object"]}, "execution_count": 85, "metadata": {}, "output_type": "execute_result"}], "source": ["# Check Data Types\n", "quake_data.dtypes"]}, {"cell_type": "markdown", "metadata": {}, "source": ["The column with date and time information is imported as a `datetime` data type."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Split into multiple columns\n", "To split a column with date and time information into separate columns, [`Series.dt`](https://pandas.pydata.org/pandas-docs/stable/reference/series.html#api-series-dt) can be used to access the values of the series such as year, month, day etc."]}, {"cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": ["# Create new columns\n", "quake_data['date'] = quake_data['datetime'].dt.date\n", "quake_data['time'] = quake_data['datetime'].dt.time\n", "quake_data['year'] = quake_data['datetime'].dt.year\n", "quake_data['month'] = quake_data['datetime'].dt.month\n", "quake_data['day'] = quake_data['datetime'].dt.day\n", "quake_data['hour'] = quake_data['datetime'].dt.hour\n", "quake_data['minute'] = quake_data['datetime'].dt.minute\n", "quake_data['second'] = quake_data['datetime'].dt.second"]}, {"cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>datetime</th>\n", "      <th>latitude</th>\n", "      <th>longitude</th>\n", "      <th>depth</th>\n", "      <th>magnitude</th>\n", "      <th>date</th>\n", "      <th>time</th>\n", "      <th>year</th>\n", "      <th>month</th>\n", "      <th>day</th>\n", "      <th>hour</th>\n", "      <th>minute</th>\n", "      <th>second</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1973-08-09 02:18:00</td>\n", "      <td>40.260</td>\n", "      <td>-124.233</td>\n", "      <td>2.0</td>\n", "      <td>5.1</td>\n", "      <td>1973-08-09</td>\n", "      <td>02:18:00</td>\n", "      <td>1973</td>\n", "      <td>8</td>\n", "      <td>9</td>\n", "      <td>2</td>\n", "      <td>18</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1976-11-27 02:49:00</td>\n", "      <td>40.998</td>\n", "      <td>-120.447</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "      <td>1976-11-27</td>\n", "      <td>02:49:00</td>\n", "      <td>1976</td>\n", "      <td>11</td>\n", "      <td>27</td>\n", "      <td>2</td>\n", "      <td>49</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>1977-02-22 06:24:00</td>\n", "      <td>38.478</td>\n", "      <td>-119.287</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "      <td>1977-02-22</td>\n", "      <td>06:24:00</td>\n", "      <td>1977</td>\n", "      <td>2</td>\n", "      <td>22</td>\n", "      <td>6</td>\n", "      <td>24</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>1978-09-04 21:54:00</td>\n", "      <td>38.814</td>\n", "      <td>-119.811</td>\n", "      <td>14.0</td>\n", "      <td>5.2</td>\n", "      <td>1978-09-04</td>\n", "      <td>21:54:00</td>\n", "      <td>1978</td>\n", "      <td>9</td>\n", "      <td>4</td>\n", "      <td>21</td>\n", "      <td>54</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>1979-10-07 20:54:00</td>\n", "      <td>38.224</td>\n", "      <td>-119.348</td>\n", "      <td>11.0</td>\n", "      <td>5.2</td>\n", "      <td>1979-10-07</td>\n", "      <td>20:54:00</td>\n", "      <td>1979</td>\n", "      <td>10</td>\n", "      <td>7</td>\n", "      <td>20</td>\n", "      <td>54</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             datetime  latitude  longitude  depth  magnitude        date  \\\n", "0 1973-08-09 02:18:00    40.260   -124.233    2.0        5.1  1973-08-09   \n", "1 1976-11-27 02:49:00    40.998   -120.447    5.0        5.0  1976-11-27   \n", "2 1977-02-22 06:24:00    38.478   -119.287    5.0        5.0  1977-02-22   \n", "3 1978-09-04 21:54:00    38.814   -119.811   14.0        5.2  1978-09-04   \n", "4 1979-10-07 20:54:00    38.224   -119.348   11.0        5.2  1979-10-07   \n", "\n", "       time  year  month  day  hour  minute  second  \n", "0  02:18:00  1973      8    9     2      18       0  \n", "1  02:49:00  1976     11   27     2      49       0  \n", "2  06:24:00  1977      2   22     6      24       0  \n", "3  21:54:00  1978      9    4    21      54       0  \n", "4  20:54:00  1979     10    7    20      54       0  "]}, "execution_count": 87, "metadata": {}, "output_type": "execute_result"}], "source": ["# Check dataset\n", "quake_data.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["New columns have been created for various date and time information."]}, {"cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime     datetime64[ns]\n", "latitude            float64\n", "longitude           float64\n", "depth               float64\n", "magnitude           float64\n", "date                 object\n", "time                 object\n", "year                  int64\n", "month                 int64\n", "day                   int64\n", "hour                  int64\n", "minute                int64\n", "second                int64\n", "dtype: object"]}, "execution_count": 88, "metadata": {}, "output_type": "execute_result"}], "source": ["# Check data types\n", "quake_data.dtypes"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Notice that `date` column is of `object` data type. It can be easily converted to a `datetime` object using `pd.to_datetime`."]}, {"cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [{"data": {"text/plain": ["datetime     datetime64[ns]\n", "latitude            float64\n", "longitude           float64\n", "depth               float64\n", "magnitude           float64\n", "date         datetime64[ns]\n", "time                 object\n", "year                  int64\n", "month                 int64\n", "day                   int64\n", "hour                  int64\n", "minute                int64\n", "second                int64\n", "dtype: object"]}, "execution_count": 89, "metadata": {}, "output_type": "execute_result"}], "source": ["quake_data['date'] = pd.to_datetime(quake_data['date'])\n", "quake_data.dtypes"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Combine columns with Date/Time information\n", "Consider a scenario where the data did not have a `datetime` column  but the `year, month, day, hour, minute, second` date and time elements were stored as individual columns as shown below. "]}, {"cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>latitude</th>\n", "      <th>longitude</th>\n", "      <th>depth</th>\n", "      <th>magnitude</th>\n", "      <th>date</th>\n", "      <th>time</th>\n", "      <th>year</th>\n", "      <th>month</th>\n", "      <th>day</th>\n", "      <th>hour</th>\n", "      <th>minute</th>\n", "      <th>second</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>40.260</td>\n", "      <td>-124.233</td>\n", "      <td>2.0</td>\n", "      <td>5.1</td>\n", "      <td>1973-08-09</td>\n", "      <td>02:18:00</td>\n", "      <td>1973</td>\n", "      <td>8</td>\n", "      <td>9</td>\n", "      <td>2</td>\n", "      <td>18</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>40.998</td>\n", "      <td>-120.447</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "      <td>1976-11-27</td>\n", "      <td>02:49:00</td>\n", "      <td>1976</td>\n", "      <td>11</td>\n", "      <td>27</td>\n", "      <td>2</td>\n", "      <td>49</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>38.478</td>\n", "      <td>-119.287</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "      <td>1977-02-22</td>\n", "      <td>06:24:00</td>\n", "      <td>1977</td>\n", "      <td>2</td>\n", "      <td>22</td>\n", "      <td>6</td>\n", "      <td>24</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>38.814</td>\n", "      <td>-119.811</td>\n", "      <td>14.0</td>\n", "      <td>5.2</td>\n", "      <td>1978-09-04</td>\n", "      <td>21:54:00</td>\n", "      <td>1978</td>\n", "      <td>9</td>\n", "      <td>4</td>\n", "      <td>21</td>\n", "      <td>54</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>38.224</td>\n", "      <td>-119.348</td>\n", "      <td>11.0</td>\n", "      <td>5.2</td>\n", "      <td>1979-10-07</td>\n", "      <td>20:54:00</td>\n", "      <td>1979</td>\n", "      <td>10</td>\n", "      <td>7</td>\n", "      <td>20</td>\n", "      <td>54</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   latitude  longitude  depth  magnitude       date      time  year  month  \\\n", "0    40.260   -124.233    2.0        5.1 1973-08-09  02:18:00  1973      8   \n", "1    40.998   -120.447    5.0        5.0 1976-11-27  02:49:00  1976     11   \n", "2    38.478   -119.287    5.0        5.0 1977-02-22  06:24:00  1977      2   \n", "3    38.814   -119.811   14.0        5.2 1978-09-04  21:54:00  1978      9   \n", "4    38.224   -119.348   11.0        5.2 1979-10-07  20:54:00  1979     10   \n", "\n", "   day  hour  minute  second  \n", "0    9     2      18       0  \n", "1   27     2      49       0  \n", "2   22     6      24       0  \n", "3    4    21      54       0  \n", "4    7    20      54       0  "]}, "execution_count": 90, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create data\n", "quake_data.drop(columns=['datetime'], inplace=True)\n", "quake_data.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In such a scenario, a `datetime` object can be easily created by using the `pd.to_to_datetime` method. The method combines date and time information in various columns and returns a `datetime64` object."]}, {"cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>latitude</th>\n", "      <th>longitude</th>\n", "      <th>depth</th>\n", "      <th>magnitude</th>\n", "      <th>date</th>\n", "      <th>time</th>\n", "      <th>year</th>\n", "      <th>month</th>\n", "      <th>day</th>\n", "      <th>hour</th>\n", "      <th>minute</th>\n", "      <th>second</th>\n", "      <th>new_datetime</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>40.260</td>\n", "      <td>-124.233</td>\n", "      <td>2.0</td>\n", "      <td>5.1</td>\n", "      <td>1973-08-09</td>\n", "      <td>02:18:00</td>\n", "      <td>1973</td>\n", "      <td>8</td>\n", "      <td>9</td>\n", "      <td>2</td>\n", "      <td>18</td>\n", "      <td>0</td>\n", "      <td>1973-08-09 02:18:00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>40.998</td>\n", "      <td>-120.447</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "      <td>1976-11-27</td>\n", "      <td>02:49:00</td>\n", "      <td>1976</td>\n", "      <td>11</td>\n", "      <td>27</td>\n", "      <td>2</td>\n", "      <td>49</td>\n", "      <td>0</td>\n", "      <td>1976-11-27 02:49:00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>38.478</td>\n", "      <td>-119.287</td>\n", "      <td>5.0</td>\n", "      <td>5.0</td>\n", "      <td>1977-02-22</td>\n", "      <td>06:24:00</td>\n", "      <td>1977</td>\n", "      <td>2</td>\n", "      <td>22</td>\n", "      <td>6</td>\n", "      <td>24</td>\n", "      <td>0</td>\n", "      <td>1977-02-22 06:24:00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>38.814</td>\n", "      <td>-119.811</td>\n", "      <td>14.0</td>\n", "      <td>5.2</td>\n", "      <td>1978-09-04</td>\n", "      <td>21:54:00</td>\n", "      <td>1978</td>\n", "      <td>9</td>\n", "      <td>4</td>\n", "      <td>21</td>\n", "      <td>54</td>\n", "      <td>0</td>\n", "      <td>1978-09-04 21:54:00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>38.224</td>\n", "      <td>-119.348</td>\n", "      <td>11.0</td>\n", "      <td>5.2</td>\n", "      <td>1979-10-07</td>\n", "      <td>20:54:00</td>\n", "      <td>1979</td>\n", "      <td>10</td>\n", "      <td>7</td>\n", "      <td>20</td>\n", "      <td>54</td>\n", "      <td>0</td>\n", "      <td>1979-10-07 20:54:00</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   latitude  longitude  depth  magnitude       date      time  year  month  \\\n", "0    40.260   -124.233    2.0        5.1 1973-08-09  02:18:00  1973      8   \n", "1    40.998   -120.447    5.0        5.0 1976-11-27  02:49:00  1976     11   \n", "2    38.478   -119.287    5.0        5.0 1977-02-22  06:24:00  1977      2   \n", "3    38.814   -119.811   14.0        5.2 1978-09-04  21:54:00  1978      9   \n", "4    38.224   -119.348   11.0        5.2 1979-10-07  20:54:00  1979     10   \n", "\n", "   day  hour  minute  second        new_datetime  \n", "0    9     2      18       0 1973-08-09 02:18:00  \n", "1   27     2      49       0 1976-11-27 02:49:00  \n", "2   22     6      24       0 1977-02-22 06:24:00  \n", "3    4    21      54       0 1978-09-04 21:54:00  \n", "4    7    20      54       0 1979-10-07 20:54:00  "]}, "execution_count": 91, "metadata": {}, "output_type": "execute_result"}], "source": ["# Create new datetime column\n", "quake_data['new_datetime'] = pd.to_datetime(quake_data[[\"year\", \"month\", \"day\", \"hour\", \"minute\", \"second\"]])\n", "quake_data.head()"]}, {"cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [{"data": {"text/plain": ["latitude               float64\n", "longitude              float64\n", "depth                  float64\n", "magnitude              float64\n", "date            datetime64[ns]\n", "time                    object\n", "year                     int64\n", "month                    int64\n", "day                      int64\n", "hour                     int64\n", "minute                   int64\n", "second                   int64\n", "new_datetime    datetime64[ns]\n", "dtype: object"]}, "execution_count": 92, "metadata": {}, "output_type": "execute_result"}], "source": ["# Check data types\n", "quake_data.dtypes"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Conclusion"]}, {"cell_type": "markdown", "metadata": {}, "source": ["In this part of the guide series, you have seen in detail how to work with Time Series data. Here, we briefly introduced date and time data types in native python and then focused on date/time data in Pandas. You have seen how `date_range` can be created with frequencies. We discussed various indexing and selection operations on time series data. Next, we introduced time series specific operations, such as `resmaple()`, `shift()`, `tshift()` and `rolling()`. We also briefly discussed time zones and operating on data with different time zones."]}, {"cell_type": "markdown", "metadata": {}, "source": ["## References"]}, {"cell_type": "markdown", "metadata": {}, "source": ["[1] Wes McKinney. 2017. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd. ed.). O'Reilly Media, Inc.    \n", "    \n", "[2] Jake VanderPlas. 2016. Python Data Science Handbook: Essential Tools for Working with Data (1st. ed.). O'Reilly Media, Inc.    "]}], "metadata": {"anaconda-cloud": {}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3"}, "livereveal": {"scroll": true}, "toc": {"base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {"height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "202px"}, "toc_section_display": true, "toc_window_display": true}}, "nbformat": 4, "nbformat_minor": 2}